Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein–Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis. In the current era, Artificial Intelligence (AI) forms a transformative underpinning of our scientific progress ,2,3. LevDeep learning has demonstrated exceptional prowess in uncovering complex patterns within high-dimensional data, resulting in ground-breaking applications across various domains ,20,21. BPPIs, pivotal elements in cellular processes, play an instrumental role in various biological functions ,26,27,28One groundbreaking application of deep learning in protein studies is embodied by AlphaFold , a remarAlphaFold utilizes a deep-learning-based approach to predict protein structure, a problem of profound significance in biology. The AI model has been meticulously trained on a wealth of data derived from the Protein Data Bank, integrating a vast multitude of known protein structures into its learning framework. The system leverages this training to predict the arrangement of amino acids within a protein, generating a comprehensive three-dimensional model that illuminates the protein’s spatial conformation.With the emerging developments in deep learning, an increasing number of research endeavors have explored its application to PPIs. Deep learning holds the promise of revolutionizing PPI prediction, ushering in an era of highly accurate, efficient, and insightful computational methodologies. This paper, therefore, provides a comprehensive review of the most recent literature that employs deep learning for PPI analysis, with a particular focus on works published during the period of 2021–2023.In an era where deep learning technologies are experiencing unprecedented growth and innovation, it is imperative to stay abreast of the most recent developments. This review, therefore, serves as a crucial resource for researchers in the field, encapsulating the state-of-the-art techniques in PPI analysis using deep learning, thereby providing insights into this rapidly evolving domain.The primary objective of the paper selection process was to ascertain the incorporation of high-quality, related research in the deep learning for PPIs domain. This was accomplished by adopting an algorithmic approach primarily hinged on the scholarly search engine, Web of Science (WOS). The search keywords were meticulously selected, focusing on crucial topics such as “deep learning”, “protein–protein interactions”, and “artificial neural network”. This was done with the intention of identifying pertinent articles for a comprehensive review. The review is strictly confined to papers published in peer-reviewed journals. This restriction was instated on account of two main reasons. First, peer-reviewed journals typically uphold the quality and reliability of the scientific literature by subjecting the papers to an intense review by experts in the field. Second, they are considered trustworthy sources for the publication of scientifically robust and influential research.Despite acknowledging the presence of preprints and conference papers in this domain, it was decided to concentrate solely on peer-reviewed journal articles. This decision was motivated by the need to enhance the reliability and validity of the review, by ensuring the inclusion of studies that have undergone an intense review process. Moreover, in order to retain the novelty and originality of the review, certain article types like review articles and perspectives were deliberately excluded. The aim was to emphasize the integration of primary research-based studies, aligning with the purpose of the review.The temporal scope of the review was restricted to articles published during the last three years, from 2021 to 2023. This timeframe was selected to guarantee the relevance and contemporaneity of the review. This allows for a thorough understanding of the most recent developments and trends in deep learning for PPIs. It is important to mention that the data collection for 2023 was conducted up to May, in line with the present timeline, thereby ensuring that the review remains concurrent with the latest advancements in the field. Throughout the data collection process, we gathered information about the number of citations and the publication log for each selected article. These details served as vital factors in appraising the scope, impact, and acceptability of the research within the scientific community.To provide a structured overview of deep learning for PPIs, the selected papers were categorized based on the objectives of the specific studies. This classification contributes to a comprehensive understanding of the varied methodologies employed in the field, thereby enhancing our understanding of the deep learning landscape for PPIs. Despite many papers aligning with multiple categories, they were assigned to a single category that best represented the main theme of the paper. An examination of the selected papers was undertaken to elucidate the utilization of deep learning methodologies for protein–protein interactions. These deep learning techniques have been enumerated in Graph Neural Networks have been exceedingly utilized for deep learning applications in numerous studies, capitalizing on graph data processing. They have been effectively employed to model PPIs, given the inherent graph-like structure of protein interaction networks. A non-negligible portion of the studies employed Convolutional Neural Networks. Capitalizing on their capacity for spatial data processing, CNNs have been utilized for deep learning purposes in PPI research. In a different vein, certain studies have exploited Representation Learning and Autoencoders for obtaining representations with deep learning, which has proven instrumental in discerning novel features and protein interaction patterns. The sequential data processing capabilities of Recurrent Neural Networks, including Long Short-Term Memory networks, have been harnessed in various studies, which underscores their utility in handling time-series data and capturing temporal dependencies, a feature especially relevant for sequential biological data such as protein sequences. Noteworthy is the application of Attention Methods and Transformers that rely on the attention mechanism and position-specific encoding for deep learning. Their ability to model long-range interactions and complex dependencies makes them suitable for tasks such as predicting PPIs. Moreover, Multi-task and Multi-modal Learning methods have found their application in a number of studies. These methods can effectively handle multiple tasks or data types simultaneously, thus, they can simultaneously predict multiple types of PPIs or utilize different kinds of biological data. Several studies have adopted Transfer Learning approaches, reaping the benefits of pre-trained deep learning models for feature extraction, and thus reducing the requirement for vast quantities of training data.Generic/Applications (including Multi-Layer Perceptrons (MLPs)) and Others encompasses a broad range of models and applications, including some that do not specifically fit into the aforementioned categories or those that use PPIs as inputs for deep learning models. This signifies the breadth and diversity of deep learning applications in the field of PPIs. The landscape of deep learning for PPIs is marked by a diverse array of methodologies, each having its unique capabilities and advantages, which have been adeptly utilized in various studies for unveiling the complex patterns of protein interactions.The category of The journals in which the selected papers were published provide insight into the scientific communities that are actively engaged in deep learning for PPIs. An analysis of the publication outlets for these articles can also shed light on their impact and reach within the scientific community.The ’Briefings in Bioinformatics’ and ’BMC Bioinformatics’ journals both housed 12 publications, each comprising 10.1% of the total reviewed articles. This underscores their substantial contribution to the dissemination of research in this field.The ’IEEE-ACM Transactions on Computational Biology and Bioinformatics’ journal, with seven articles, constitutes 5.9% of the total publications. This suggests a substantial interest in this topic within the computational biology and bioinformatics community.The ’Computational and Structural Biotechnology Journal’ and ’Frontiers in Genetics’, each with four articles, represents 3.4% of the total papers reviewed, indicating their role in the research landscape of deep learning for PPIs.A host of other journals, each with two publications, embody 1.7% of the total, including prestigious titles like ’Science’ and ’Nature Machine Intelligence’, highlighting the interdisciplinary and cross-field interest in this research area.The dynamics of publications in the domain of deep learning for PPIs can be analyzed in terms of temporal distribution. As illustrated in In 2021, a total of 40 studies were published that applied deep learning methods to PPIs. This represents a significant contribution to the field, reflecting a mature state of research interest. The following year, 2022, witnessed a substantial surge in the number of publications, amounting to 56. This represents an approximately 40% increase from the previous year. This rapid growth signals the emerging enthusiasm and considerable advancements in the application of deep learning methods to PPIs.As for 2023, until May, there have already been 23 papers published. If the current publication rate persists throughout the year, the total number of publications in 2023 is projected to surpass that of the previous years. This trend underlines the continuous evolution of the field, as well as the persistent pursuit for improved methodologies for understanding and leveraging PPIs using deep learning techniques.An examination of citation distribution offers insights into the reception and influence of publications within the sphere of deep learning for PPIs. Statistical metrics, such as median and mean, can provide a robust summary of the overall citation landscape. The median number of citations for these publications is recorded as 2, while the mean is observed to be slightly higher at 5.3. A notable point is the high number of studies that have not yet been cited, implying that these are relatively recent contributions, or perhaps they have yet to be discovered or appreciated by the wider research community. This lack of citations may also be an artifact of the current data collection process, as data for 2023 is not fully collected and updated by the WoS.The disparity between the mean and median citation count can be indicative of a skewed distribution, likely due to a small number of highly cited papers. It highlights the breadth of research impact, where a handful of studies may have profoundly influenced the field, while the majority of studies are yet to make a substantial impact. These findings, combined with the awareness that the field is still young and in a constant state of evolution, paint a promising picture for the future of deep learning applications in PPIs. It reinforces the idea that this research area is rich with opportunity and potential for transformative discoveries.Overall, the increasing trend in the number of publications underscores the vitality of this research domain and implies the potential for future development. This continuous growth reflects the ongoing refinement of deep learning methods applied to PPIs and the recognition of their valuable contributions in biological and computational research.The emergence and development of historical deep learning methodologies for PPI analysis have significantly facilitated the comprehensive understanding of complex cellular processes. They have been instrumental in enabling thorough investigation and prediction of these interactions. In this section, two representative frameworks (PIPR and DPPI) and their limitations are discussed.The PIPR framework  introducThe DPPI method  introducGraph Neural Networks (GNNs) ,154,155 In essence, a graph can be seen as a collection of nodes and edges, where nodes represent entities , and edges denote relationships or interactions . GNNs take advantage of this structured data format by applying various forms of convolutions directly on the graph, enabling them to learn from both local node features and the broader network topology. This ability is particularly useful in the study of PPIs, where the biological significance of an interaction often depends not only on the properties of the interacting proteins but also on their position and role within the larger protein network.The unique capacity of GNNs to exploit the underlying structure of graph data is achieved through several key mechanisms. Firstly, GNNs use neighborhood aggregation or message-passing frameworks, wherein each node in the graph gathers information from its local neighbors to update its state. This allows GNNs to incorporate local context into node representations, thereby capturing the immediate interaction dynamics in PPIs. Secondly, through multiple rounds of these aggregations, GNNs can learn increasingly abstract representations of nodes, thereby modeling higher-order interaction effects and uncovering complex interaction patterns.Various types of GNNs have been employed in the study of PPIs, with each offering unique advantages. Graph Convolutional Networks (GCNs) ,157,158,Research leveraging GNNs for PPI prediction spans a wide range of applications, from identifying specific interaction sites on proteins, predicting the existence of interactions between protein pairs, to classifying proteins based on their interaction profiles. These studies typically involve formulating the PPI problem as a graph-based learning task, such as node classification, link prediction, or graph classification, and employing suitable GNN architectures to solve it.Recent studies have witnessed a prominent trend in utilizing GNNs for PPI predictions. These studies have explored various models and techniques, aiming to enhance the accuracy and efficiency of PPI prediction tasks. Notably, researchers have focused on leveraging GNNs, such as augmented GATs and GCNs, to capture structural invariance, learn graph representations, and improve prediction performance. Additionally, the integration of multimodal data sources, biological features, and prior knowledge has emerged as a significant aspect of recent research efforts. These studies have demonstrated remarkable advancements in predicting PPIs and utilizing PPI information for various predictive tasks, reinforcing the critical role of deep learning methods, particularly GNNs and GCNs, in advancing our understanding of PPIs and their implications in biological systems. Continued research and methodological advancements are expected to drive further progress in this field. The summary of recent studies can be observed in Albu et al.  presenteBaranwal et al.  offered Quadrini et al.  used GraWilliams et al.  developeAzadifar and Ahmadi  introducThe range and depth of these studies underscore the crucial role deep learning methods, particularly GNNs and GCNs, continue to play in advancing PPI predictions. With ongoing research and methodological enhancements, the future promises continued progress in understanding and predicting PPIs and their influence on biological systems.Convolutional Neural Networks (CNNs) ,163,164 A CNN typically consists of multiple layers, which may include convolutional layers, pooling layers, and fully connected layers. The distinctive feature of CNNs is the convolutional layer that performs a convolution operation. In the context of a 1D sequence such as a protein sequence, a convolution involves a filter (or kernel) moving across the input sequence and performing an element-wise multiplication and sum operation, thereby capturing local dependencies within the sequence. In the case of 2D data like images or 3D data like protein structures, similar operations are performed but in two or three dimensions, respectively.This local receptive field, embodied in the convolution operation, allows the model to automatically and adaptively learn spatial hierarchies of features. For instance, lower layers of the network might learn to recognize simple patterns such as certain sequence motifs in a protein sequence, while higher layers could learn to recognize more complex patterns based on the lower-level features, analogous to recognizing complex shapes or objects from simple edges in image data.Pooling layers within a CNN perform a down-sampling operation along the spatial dimensions, which helps to make the representation invariant to small translations and reduce the computational complexity. The fully connected layers typically come towards the end of the network and can be seen as a traditional multi-layer perceptron that uses the high-level features extracted by the preceding convolutional and pooling layers to perform classification or regression.In PPIs, CNNs are often employed to learn from protein sequence or structure data, where they can effectively capture local dependencies and hierarchies of biological features. For instance, studies in this category might involve predicting whether a given pair of proteins interacts based on their sequence or structural features, or identifying the specific sites of interaction on a given protein.Additionally, CNNs have been combined with other types of networks, such as RNNs or attention networks, to better model complex dependencies in the data. These hybrid models allow researchers to leverage the strengths of multiple architectures to improve PPI prediction performance.Recent studies have showcased notable trends in the application of CNNs for PPI analysis. These studies have explored diverse models and approaches, aiming to enhance the accuracy and effectiveness of PPI prediction tasks. Researchers have developed deep residual neural networks, ensemble residual CNNs, and Siamese-ensemble models, among others, to leverage sequence-driven features, improve prediction performance, and circumvent local optima. Additionally, the application of CNNs in protein docking, binding site prediction, and human-virus PPI analysis has demonstrated significant advancements. The integration of deep learning frameworks, such as recurrent CNNs and three-track neural networks, has proven valuable in predicting protein interactions, phosphorylation sites, and protein–peptide binding sites. Moreover, advancements in protein sequence encoding formats and graph-regularized CNNs have contributed to the coherence and biological interpretation of gene clusters in spatial gene expression analysis. The range and depth of these studies highlight the importance of CNNs in advancing our understanding and prediction of PPIs, emphasizing their potential for future research endeavors. Chen et al.  designedYuan et al.  introducHu et al.  presenteGuo et al.  developeKozlovskii and Popov  developeWang et al.  proposedA core challenge in PPIs and related biological properties using deep learning approaches is the representation of the protein sequences or structures. Representation learning ,166,167,In the context of protein studies, representation learning methods have been used to transform protein sequence and structure information into meaningful features that capture the biological properties of the proteins. These methods can range from simple techniques such as one-hot encoding or count-based representations, to more sophisticated methods based on word embeddings like Word2Vec ,169,170,Representation learning plays a significant role in PPI analysis by efficiently encoding and representing protein sequences or structures. This involves transforming raw biological data into an informative, reduced-dimensional format that can facilitate further computational analysis and predictive modeling. In the context of PPI studies, this encompasses the development of methodologies and models that convert protein sequences or structural information into meaningful features that capture the essential biological properties of proteins.The autoencoder ,172,173,By training an autoencoder to minimize the difference between the original and the reconstructed data (known as reconstruction error), we can use the learned lower-dimensional representation as a new feature set for our data. This approach has been particularly useful for PPI prediction, where the complexity and high-dimensionality of protein data often make it difficult to devise hand-crafted features.Autoencoders can take on various forms depending on the specific use case. For example, denoising autoencoders  are traiA wide range of studies involving PPIs fall within the purview of representation learning and autoencoders. This includes work that uses autoencoders or other representation learning methods to transform protein sequence or structural data into a format suitable for PPI prediction, studies that use these methods to predict specific properties related to PPIs, like interaction sites or interaction types, and those that integrate these methods with other machine learning or deep learning techniques to improve PPI prediction performance.Recent studies have highlighted the significant role of autoencoders and representation learning in PPI analysis. Researchers have developed innovative frameworks and models that leverage autoencoders to encode protein structures and primary sequences, leading to enhanced computational efficiency and low complexity. Additionally, the integration of graph autoencoders and deep sequence features has demonstrated superior performance in predicting abnormal phenotype-human protein associations. Autoencoders have also been applied in ensemble models for PPI prediction, utilizing separate autoencoders for positive and negative interactions. Representation learning techniques, including hashing methods, have emerged as effective approaches for reducing time complexity in predicting PPI relationships. Deep learning models directly utilizing protein sequences have proven highly accurate, even with limited training data, providing valuable insights into protein characterization. Researchers have also explored interdisciplinary applications, such as viral-host PPI prediction and SARS-CoV2-human host protein interaction analysis, where deep learning methodologies have showcased remarkable advancements. Moreover, the incorporation of GO terms and attention mechanisms has led to the development of models that capture deep semantic relations and outperform traditional semantic similarity measures in PPI prediction. These recent studies collectively underscore the importance of autoencoders and representation learning techniques in advancing our understanding and prediction of PPIs. Recent studies are summarized in Several models have been proposed to predict pairwise PPIs. Ieremie et al.  proposedSeveral works have focused on the prediction of PPI networks. Hasibi and Michoel  demonstrPredicting the sites of protein–protein interactions has also been a subject of focus. Wang et al.  introducSeveral studies have applied representation learning and autoencoders for auxiliary PPI prediction tasks. Liu et al.  designedCzibula et al.  introducRecurrent Neural Networks (RNNs) are a class of artificial neural networks designed to recognize patterns in sequences of data, such as text, speech, or, in this case, protein sequences ,176,177.An RNN contains a layer of hidden units, whose activations are calculated based on the current input and the previous hidden state. This recurrent connection allows information to be passed along from one step in the sequence to the next, creating an internal state of the network that allows it to exhibit dynamic temporal behavior.One of the major variants of RNNs, particularly effective for PPI prediction, is the Long Short-Term Memory (LSTM) network . LSTMs wThe cell state acts as a kind of conveyor belt, allowing important information to be carried forward with minimal modification, while the input, forget, and output gates control the extent to which new information is added, old information is removed, and the current state is revealed, respectively. This mechanism allows LSTMs to learn long-term dependencies, making them particularly effective when dealing with protein sequences, which can be quite long and exhibit complex dependencies.The utility of RNNs, and LSTMs in particular, for the prediction of PPIs is related to the sequential and interdependent nature of protein sequences. The prediction of whether two proteins interact is often dependent not just on the individual amino acids in each protein, but also on the order of these amino acids, and the broader context they are in.Given this inherent suitability, many studies in PPI prediction use RNNs as a fundamental part of their methodology. This might involve using RNNs to learn a representation of protein sequences, which is then used as input to a prediction algorithm, or integrating RNNs with other machine learning techniques to create hybrid models that combine the strengths of different approaches.Recent studies have demonstrated notable trends in the utilization of RNNs for PPI analysis. Researchers have developed innovative strategies, such as bidirectional LSTM models, to generate relevant protein sequences and incorporate complex network analysis. Ensembles of deep learning models, including LSTM-based approaches, have showcased superior performance in PPI site prediction by integrating diverse features and auxiliary information. Furthermore, the application of regularization techniques during training has proven effective in improving the accuracy of PPI prediction models. RNNs have also been instrumental in bridging the gap between PPI research and the understanding of complex interactions, such as plant-pathogen interactions and virus–host interactions. Machine learning models incorporating frustration indices, structural features, and word2vec analysis of amino acid sequences have demonstrated promising results in PPI prediction. Deep learning methods have been successfully applied to predict protein interactions related to SARS-CoV-2 and to identify essential proteins. The incorporation of novel features, ensemble models, and network embedding techniques has further improved the accuracy and performance of RNN-based PPI prediction models. Additionally, deep learning approaches have been leveraged for PPI network alignment and sequence-based protein–protein binding predictions, yielding remarkable results and outperforming traditional machine learning methods. These recent studies collectively highlight the significance of RNNs in advancing our understanding and prediction of PPIs, paving the way for further research and innovation in the field. Several models have been proposed to predict pairwise PPIs using RNNs. Alakus and Turkoglu  proposedAybey and Gumus  proposedFor PPI network prediction, Mahdipour et al.  introducSeveral works have utilized RNNs for auxiliary PPI prediction tasks. Zeng et al.  introducThe attention mechanism and transformer networks represent breakthroughs in the field of deep learning and have proven to be highly effective for a variety of applications ,16,17,18The attention mechanism was introduced to improve the performance of recurrent neural network architectures, particularly in tasks dealing with sequences of data. The central idea behind the attention mechanism is to allow the model to focus on different parts of the input sequence when producing an output. This is done by assigning weights, or “attention scores,” to different elements in the sequence, which determine the amount of attention each element should receive. The attention scores are computed dynamically and depend on the context within which the data is processed. This concept allows the model to ’focus’ on relevant parts of the input for each step in the output sequence, thereby improving its ability to handle long sequences and complex dependencies.The transformer network, on the other hand, represents a new class of model architectures that exclusively use attention mechanisms, eliminating the need for recurrence altogether. Proposed by Vaswani et al. , the traIn the multi-head attention mechanism, the model computes attention scores multiple times with different learned linear projections of the input. This allows the model to focus on different types of information in different parts of the input sequence. Meanwhile, the position-wise feed-forward networks are applied identically to each position, allowing the model to learn complex patterns within the sequence.For the prediction of PPIs, these methodologies provide significant advantages. Due to their ability to capture dependencies regardless of their distance in the sequence, attention mechanisms and transformers can efficiently process protein sequences, which are inherently sequential and can exhibit complex, long-range dependencies. This makes them well-suited to tasks that involve recognizing patterns in protein sequences to predict whether and how proteins interact.Given their effectiveness and versatility, attention mechanisms and transformer models have been used in a variety of ways in PPI prediction. Some studies employ these methods to learn robust representations of protein sequences, while others incorporate them into more complex models designed to leverage different types of biological information for PPI prediction.Recent studies have showcased the growing popularity of attention mechanisms and Transformer models in the field of PPI prediction. Researchers have explored innovative approaches that integrate attention mechanisms into deep learning architectures to improve the accuracy and performance of PPI prediction models. These attention-based models have demonstrated remarkable results across various datasets and tasks. The integration of attention mechanisms with LSTM, convolutional, and self-attention layers has yielded powerful hybrid models for PPI prediction. Moreover, the utilization of Transformer neural network architectures, originally designed for natural language processing, has shown great potential in pre-training sequence representations and fine-tuning them for specific PPI-related tasks. The effectiveness of attention networks and Transformer models is evident in their superior performance compared to existing computational methods for PPI site prediction, protein interaction prediction, bio-entity relation extraction, and protein interaction network reconstruction. These recent studies highlight the significance of attention mechanisms and Transformer models in advancing our understanding and prediction of PPIs, paving the way for further research and development in the field. The findings of recent studies are outlined in Several studies have leveraged the power of attention mechanisms and transformers for pairwise PPI prediction. Asim et al.  proposedIn the domain of PPI site prediction, Tang et al.  proposedFor PPI network prediction, Zhu et al.  introducSeveral models have been developed for auxiliary PPI prediction tasks. Li et al.  utilizedBaek et al.  utilizedThe utilization of multi-task and multi-modal deep learning models ,180 has Multi-task learning models are designed to improve learning efficiency and predictive performance by learning multiple related tasks concurrently . The funMulti-modal deep learning models , on the In the context of PPI prediction, these methodologies offer several advantages. Multi-task learning models can learn from multiple related tasks , thereby leveraging the shared information among tasks to improve prediction performance. Similarly, multi-modal models can integrate information from multiple sources , thereby leveraging the complementarity among different types of data to obtain a more comprehensive understanding of the protein interaction mechanisms.Given their potential for dealing with complex and heterogeneous PPI prediction problems, multi-task and multi-modal deep learning models have found broad applications in the PPI field. They have been used to leverage multiple related tasks or multiple sources of information, improving prediction performance and providing a more comprehensive understanding of the protein interaction mechanisms.Recent studies have focused on the development of multi-task or multi-modal deep learning models to enhance the prediction of PPIs. These models aim to leverage multiple sources of information, such as protein sequences, structural annotations, gene features, multiomics data, and GO information, to improve the accuracy and robustness of PPI predictions. By incorporating various tasks or modalities into the learning process, these models have demonstrated superior performance compared to single-task methods. Additionally, efforts have been made to enhance the interpretability of deep learning models by incorporating explainable features or methodologies. These advancements in multi-task and multi-modal deep learning approaches have opened up new possibilities for predicting PPIs and expanding our understanding of complex biological interactions in diverse areas, including disease research and infectious disease studies. A range of models have been proposed to predict pairwise PPIs. For instance, Capel et al.  proposedSeveral models have been developed to predict PPI networks. Peng et al.  introducIn the PPI site prediction, Capel et al.  have demA variety of models have been proposed for auxiliary PPI prediction tasks. Linder et al.  introducTransfer learning ,182,183,In the context of a typical transfer learning framework, the initial training phase occurs on a source task or tasks, from which a model learns generic representations. Once the model is trained on the source task, the learned knowledge, typically in the form of model parameters or learned representations, is then transferred to the target task. This transfer step can be realized in different ways. One common approach is to use the trained model on the source task as a pre-trained model for the target task, either by fine-tuning the entire model or by freezing some layers  and training only the remaining ones .There are several key reasons why transfer learning can be advantageous for PPI prediction. One fundamental reason is that it enables the use of large amounts of labeled data available for some tasks (source tasks) to assist the learning process in other tasks (target tasks) that have limited labeled data. This is particularly useful in the field of bioinformatics where obtaining labeled data can be expensive and time-consuming. Additionally, transfer learning can help to mitigate the risk of overfitting on small datasets by introducing prior knowledge into the model.Transfer learning models can be categorized into different types based on the nature of the source and target tasks and the relationship between them. Examples of categories include inductive transfer learning, transductive transfer learning, and unsupervised transfer learning. In the field of PPIs, the use of transfer learning is typically seen in tasks where there is a need to leverage knowledge from well-studied organisms or proteins to less-studied ones, or from one type of protein interaction to another.A host of studies ,112,113 Among these, Chen et al.  put forwAlong the same lines, Zhang et al.  developeTurning attention towards the identification and characterization of protein structural sites, Derry and Altman  proposedIn terms of interaction prediction between human and virus proteins, Yang et al.  presenteAs the field of PPIs continues to grow, a variety of innovative and promising research topics are coming to the fore. These topics often revolve around novel applications of machine learning techniques or aim to address more specific and complex aspects of PPI prediction. This section provides an overview of some of these emerging topics in the field of PPIs, highlighting the broad scope and diversity of research that is currently being undertaken.One of the key emerging areas involves the prediction of specific aspects of PPIs beyond merely identifying whether an interaction occurs. This includes predicting the binding sites of PPIs, understanding residue-residue interactions across protein interfaces, and determining protein–protein association rates. Each of these topics poses unique challenges and has the potential to contribute valuable insights into the mechanisms of PPIs.Another noteworthy direction is the development of models that combine different types of features or use multiple learning techniques in a hybrid approach. These models often aim to take advantage of the strengths of different methods or to compensate for their individual weaknesses. For instance, some models may combine handcrafted and learned features, utilize both deep learning and gradient boosting methods, or integrate deep learning and reinforcement learning.An additional trend in the field pertains to the application of deep learning methods to specific subsets of PPIs. Examples include the prediction of PPIs for specific organisms, such as plants, the study of interactions between humans and viruses, or the analysis of PPIs in specific subcellular locations, such as mitochondria. In each case, the uniqueness of the application necessitates the development of specialized models and approaches.Furthermore, the rise of powerful deep learning methods, such as AlphaFold ,29, is pThere is growing interest in utilizing deep learning techniques for the analysis of protein sequences and the extraction of valuable insights from these sequences. This encompasses a wide range of tasks, from predicting interaction sites in specific types of proteins, such as transmembrane proteins, to identifying coevolution patterns in protein families.Recent studies have showcased diverse and innovative approaches for predicting PPIs using deep learning. These methods encompass various aspects, such as PPI binding site prediction, application of PPI analysis using existing tools, multi-label PPI prediction, protein docking decoys evaluation, protein interaction interface region prediction, protein complex modeling, and biomedical relation extraction. By leveraging protein sequence information, predicted structures, coevolution signals, joint multiple sequence alignments, and structural properties of proteins, these approaches have demonstrated remarkable performance improvements in PPI prediction accuracy and robustness. These advancements highlight the versatility and effectiveness of deep learning techniques in unraveling the complexities of PPIs and their implications in diverse biomedical research areas. Several studies have demonstrated the efficacy of various deep learning methods. For instance, Nikam et al. , developIn a similar vein, some researchers have focused on developing deep learning methods that leverage structural properties of proteins to improve PPI predictions. For instance, Han et al.  applied Deep learning methods have also been applied to protein complex modelling and protein function prediction. Yin et al.  benchmarA few studies have concentrated on predicting PPIs based on coevolution signals from joint multiple sequence alignments. For instance, Pei et al.  employedSeveral novel approaches have also been proposed for predicting protein–protein interactions. These include GRNN-PPI by Xu et al. , TAGPPI Lastly, several studies have been dedicated to the application of deep learning in biomedical relation extraction. Notably, Zhu et al.  proposedDespite the remarkable advancements in employing deep learning models, particularly GNNs, GCNs, CNNs, autoencoders, and representation learning for PPI prediction, several challenges persist that need to be addressed. The ability of these models to predict PPIs often hinges on the availability and quality of training data, the integration of diverse data sources, model complexity, and interpretability. These challenges must be addressed to facilitate further improvements in PPI prediction and to understand biological systems at a more granular level.One key challenge pertains to the availability and quality of PPI datasets. In several studies such as the work by Baranwal et al.  and WillMoreover, while the integration of multimodal data sources and diverse biological features has shown promise in enhancing prediction performance, as evidenced by Albu et al.  and Kim The trade-off between model complexity and interpretability is another substantial challenge. As seen in studies like Soleymani et al.  and ChenOne area requiring further exploration is the applicability of these models to emerging and interdisciplinary domains. Studies such as those conducted by Asim et al.  and Ray Another recent and arguably crucial breakthrough in PPI prediction pertains to the prediction of structural information. One of the most significant and possibly the most challenging tasks in this regard is the prediction of the structure of protein–protein complexes. The application of deep learning has also extended to this challenge, with tools such as AlphaFold2 and its variants leading the charge .AlphaFold and AlphaFold2 has shown remarkable levels of accuracy in modelling single chain protein structures ,184. ThiDespite these achievements, accurately predicting the structures of protein–protein complexes remains a significant challenge. AlphaFold2 and its subsequent variants, despite being state-of-the-art predictors, still show room for improvement in this area. For instance, in a recent application of AlphaFold2 for the prediction of heterodimeric protein complexes, the tool generated models with acceptable quality for only 63% of the dimers . While tGiven the ongoing challenges and limitations of current deep learning tools in predicting the structures of protein–protein complexes, several promising avenues for further research and development have emerged. One of these is the concept of “hot-spots”, regions of amino acid residues on the PPI interface that contribute significantly to binding-free energy. By focusing on these hot-spots, researchers may be able to design more effective PPI drugs, as small molecule drugs only need to target these regions to intervene in PPIs .Another promising approach is the application of GNNs to predict PPIs. For instance, a study employed GCN and GAT to predict PPIs, utilizing protein structural information and sequence features. The protein’s amino acid network, also known as the residue contact network, was represented as a graph, where each node is a residue. This graph-based approach demonstrated superior performance over previous leading methods, suggesting that GNNs can be a powerful tool for PPI prediction .The Fold-and-Dock approach has also shown potential for improving the prediction of PPIs. In this approach, two proteins are folded and docked simultaneously, which can provide more accurate results for predicting the structure of protein pairs. For instance, PconsDock, a fold-and-dock algorithm, has been developed to predict the structure of protein pairs where alternative methods fail . HoweverThese developments underline the importance of integrating various strategies and techniques to advance our ability to predict PPIs. The discovery of hot-spots, the application of GNNs, and the development of protocols represent promising directions for further advancements in the field of PPI prediction, which could significantly impact areas such as drug discovery and protein design.While the use of deep learning models for PPI prediction has witnessed considerable progress, challenges remain that need to be addressed. By tackling these issues, the future of PPI prediction looks promising, with potential impacts not only on our understanding of biological systems but also on various practical applications such as drug discovery and disease diagnosis. With ongoing research and methodological enhancements, we can anticipate further advancements in this field.In conclusion, the rapidly evolving landscape of deep learning presents a transformative platform for predicting PPIs. This synthesis of recent studies from 2021 to 2023 provides a pivotal compass in navigating the wealth of advancements that have unfolded within this highly dynamic field. As encapsulated within this review, the diversity and sophistication of deep learning techniques being applied to PPI prediction underscore this domain’s robust and innovative trajectory.The myriad of deep learning methodologies, including GNNs, CNNs, Autoencoders, RNNs, Attention Mechanisms and Transformers, Multi-task and Multi-modal Learning, and Transfer Learning, each exhibits unique merits and characteristics in the context of PPI prediction. These powerful computational tools, endowed with the capability to distill intricate patterns within vast and complex datasets, continue to revolutionize our understanding of protein interactions and, by extension, biological systems at large.This review serves as a testament to the potential of deep learning in not only facilitating the prediction of PPIs but also in unraveling the complexity inherent in their nature. It embodies a comprehensive resource for established researchers and newcomers in the field, equipping them with the necessary insights and references to propel their scientific endeavors. The fact that this domain continues to flourish at an unprecedented pace makes the timely amalgamation of these advancements within this review even more crucial.In the rapidly advancing frontiers of computational biology, it is crucial to remain cognizant of emerging methodologies and their potential applications. This review, therefore, not only provides an up-to-date perspective on the current state-of-the-art but also underscores the importance of continuous learning and adaptation in this field. As we proceed forward, these deep learning methodologies are anticipated to continue to evolve, potentially reshaping our understanding and prediction of PPIs and ushering in novel strategies for biological inquiry and therapeutic development.In the face of these evolving methodologies, it is the responsibility of the scientific community to scrutinize, validate, and contextualize these tools. Therefore, we hope this review will stimulate further discourse, innovation, and collaboration in applying deep learning techniques for PPI prediction and ultimately contribute to the acceleration of discoveries in this pivotal domain.


Spray drying technology can overcome these challenges by enhancing the properties of natural ingredients. Moreover, drug delivery systems can be flexibly designed to optimize the performance of natural ingredients. Among the various drug delivery systems, dry powder inhalation (DPI) has attracted much attention in pharmaceutical research. Therefore, this review will focus on the spray drying of natural ingredients for DPI and discuss their synthesis and application.Natural ingredients have many applications in modern medicine and pharmaceutical projects. However, they often have low solubility, poor chemical stability, and low bioavailability  Nutrition application process via dry powder inhaler (DPI) spray drying.  Nowadays, natural ingredients are becoming more and more important in medical research because they have a wide range of clinical benefits. They have rich pharmacological effects, such as anti-cancer, anti-oxidation, lowering blood lipid, inducing autophagy and reducing the side effects of drug therapy –4. Howevin vitro dissolution and permeation rates of silymarin, enhancing its bioactivity after oral administration. This system effectively preserves the antioxidant activity of silymarin and does not compromise the anti-inflammatory properties of the original extract. It seems that the anthocyanin polyphenols treated by spray drying technology have a higher retention rate and their color characteristics are also maintained. At the same time, it protects phenolic acids and flavonoids from thermal degradation  It can deliver the drug directly to the systemic circulation without passing through the liver, thus avoiding the first-pass effect and improving bioavailability ; (2) It Combined with the characteristics of natural ingredients, this paper mainly introduces the application of spray drying technology in DPIs. Spray drying technology can improve the bioavailability, stability, and solubility of drugs , 27–29. Dry powder inhalation consists of natural ingredients  and natural extracts with therapeutic properties. Its composition may vary depending on the formula of the drugs and the delivery device. Generally, natural ingredients play an important role in the safety and effectiveness of DPI as a pulmonary drug delivery method. The drugs containing natural ingredients differ according to the treatment purpose –33.Natural components are an important part of drug research and development. Since 1981, about half of the small molecule drugs approved by the US Food and Drug Administration (FDA) have come from natural sources. With the progress of science and technology, researchers pay more and more attention to the pharmacological activities of trace, insoluble, and unstable components in natural products, which used to be difficult to obtain. The advances in infrared (IR), ultraviolet-visible (UV), mass spectrometry (MS), and nuclear magnetic resonance (NMR) spectroscopy can determine the structure of active substances in natural components and promote the study of their pharmacological activities. In addition, various chromatographic separation techniques have been used to extract and purify natural components, thus allowing the mass production of trace amounts of these substances and developing them into effective treatment methods for diseases. Natural ingredients are often used in anti-cancer, anti-oxidation, anti-inflammatory, and antibacterial therapy, making their pharmacological activities an indispensable part of modern medicine. Some common natural chemicals are listed in In addition, the research progress of some common pharmacological actions of dry powder inhalers are also listed.In the field of tumor research, natural products and their derivatives play an important role in the research and development of small-molecule anticancer drugs. From the 1940s to the present, 48.6% of 175 small-molecule drugs with anticancer activity were directly derived from natural products . ExampleMany natural compounds, such as oridonin , verbascResveratrol , carotenS. aureus pneumonia. Therefore, it has the potential for pulmonary administration in the treatment of bacterial pneumonia.From 1981 to 2019, about 50% of all drugs approved by the FDA originated from natural products, and many of them had natural antibacterial functions , such asDry powder inhalation drugs are micronized, so they need to add carriers and additives to improve their fluidity and dispersibility –75, and Spray drying is used to atomize liquid materials . By atomizing the liquid into fine particles and then contacting them with a stream of hot air, the liquid is rapidly evaporated to form a dry powder or granule. The process of spray drying usually includes spraying, drying, powder separation, and collection steps. After spraying, the liquid particles enter the preheating drying chamber. In the high-temperature hot air flow, the water quickly evaporates and the particles gradually decrease, reaching a dry state. The temperature and humidity of the drying chamber can be adjusted according to the characteristics of the material and the target powder .Spray drying technology has been used in traditional pharmaceutical industries for a long time to manufacture pharmaceutical products with specific physical and chemical properties. The product is composed of porous, uniform particles  with tinFor natural product DPI preparation, according to its instability, low solubility, and bioavailability characteristics, reasonable adjustment of the above parameters can obtain the best overall performance products.Depending on the characteristics of the active pharmaceutical ingredients (API) and the demand for the formulation, we can adapt the spray drying process to prepare granular products in different forms and states, such as common nanoparticles, microspheres, liposomes, and porous nanoparticles –111. Thein vivo. This prolongs the contact time of systemic circulation, enhances the therapeutic effect of the drug, and extends the retention time of the drug in the body, thus prolonging the therapeutic effect  and curcumin in an ethanol solution improves the dispersibility of the drug. Curcumin encapsulated in casein nanoparticles has higher bioactivity than pristine curcumin, as assessed by antioxidant and cell proliferation assays . The nanc effect . In addic effect .in vitro release characteristics, reflecting their good performance in applications. They can provide a good clinical solution for some drugs that have a short half-life in circulation and are easily degraded . The following describes the operation of dry powder inhalers. Dry powder inhalers can be divided into the following types, depending on the form of the drug and how it is released: (1) Metered-dose dry powder inhalers: These inhalers release a certain amount of drug each time and usually require pressing or twisting the device to activate the drug. Examples include Advair Diskus, Flovent Diskus, and Serevent Diskus; (2) Breath-activated dry powder inhalers: These inhalers release the drug by the user’s breathing force, without the need to press or twist the device. Examples include Symbicort Turbuhaler, Asmanex Twisthaler, and Pulmicort Flexhaler. However, note that this type of inhaler may not be suitable for patients with respiratory dysfunction; (3) Single-dose dry powder inhalers: These inhalers can only be loaded with one dose of medication at a time and require the insertion of a capsule or disc before each use. Examples include Spiriva HandiHaler and Foradil Aerolizer.All three types of inhalers share a common principle. First, the drug formulation: DPIs use a dry powder formulation containing the active drug. The drug is usually micronized to ensure that the particles are small enough to penetrate the lungs. Then, respiratory drive design: DPIs are usually respiratory drive devices. Drug storage and dose preparation: The DPIs have a drug reservoir containing multiple doses of drug. Each dose is then individually packaged or prepared in the device to ensure proper dosing. Some DPIs use capsules, while others use pre-packaged blisters or spacers that the patient inhales at the same time as the drug is released for therapeutic purposes.Different types of dry powder inhalers have different advantages and disadvantages and are used in different ways. The researcher or patient may choose the appropriate type of dry powder inhaler based on clinical facts or personal preference.The advantages of dry powder inhalers over traditional methods of drug delivery, such as oral administration and injections, are obvious, especially for specific drugs, diseases, and patients. For example –131:(1)Direct action on the lesion: dry powder inhalers can act directly on the lungs through the respiratory tract, delivering the drug directly to the lesion, reducing the metabolism and side effects of the drug in the body.(2)Rapid onset of action: dry powder inhalers can be quickly absorbed into the alveoli, and then quickly enter the blood circulation, so that the drug effect works quickly.(3)High patient compliance: especially for some patients with swallowing difficulties, such as the elderly, children, and patients with digestive tract diseases, or some drugs containing bitter or offensive tastes.(4)Small amount of drug use: Compared to oral drugs, dry powder inhalers require a small amount of drug use, which can reduce the accumulation of drugs in the body and reduce the occurrence of toxic side effects of drugs.(5)Convenient and easy to use: dry powder inhalers can be used at any time and any place, easy to use and not disturbed by the external environment, especially suitable for long-term treatment of patients with chronic diseases.(6)Avoid liver first pass effect and enterohepatic circulation, increasing the bioavailability of the drug.(7)Environmentally friendly and pollution-free: dry powder inhalers do not require solvents or gases as carriers and do not produce waste or harmful substances in the process of use, which is less polluting to the environment and safer and more environmentally friendly than other drug delivery methods.And the advantages over other methods of lung drug delivery are also very obvious. DPIs are one of the four main devices used to deliver pharmaceutical preparations to the lungs, along with pressurized metered dose inhalers (PMDIs), nebulizers and soft mist inhalers (SMIs) , 132. DPPressurized metered dose inhalers were once the most widely used inhalers, but they use chlorofluorocarbon (CFC) propellant in the atomization process to deliver to lung tissue, which can lead to depletion of the ozone layer. Dry powder inhalers, on the other hand, are administered as a dry powder and do not require propellant to deliver the drug into the lungs. This makes dry powder inhalers more environmentally friendly and their reusable nature offers significant advantages in terms of resources and energy consumption.Nebulizers in in-hospital formulations are compressor driven, which is heavy, bulky, noisy and has limited portability compared to dry powder inhalers , 133. OnStudies have shown that spray drying can significantly improve the efficiency of drug absorption in the lung, improve drug distribution, avoid drug deposition, increase its bioavailability, and allow natural products to exert their target clinical effects , 137. FoAccording to BBC Research, the global pulmonary drug delivery systems market totaled USD 38.1 billion in 2017 and is expected to reach USD 47 billion by 2022, growing at a compound annual growth rate (CAGR) of 4.3% during 2017–2022. The huge market demand is aiding the research on spray drying of natural ingredients for dry powder inhalation. The application of dry powder inhalation has been widely used in formulations for the continuous delivery of atomized particles. Spray drying technology allows the production of free-flowing powders of various sizes, offering unlimited possibilities for the production of dry powder for inhalation.Dry powder inhalers exhibit good aerosol properties and can effectively deliver the drug to the lungs and obtain high pulmonary drug concentrations. Hu et al.  found thSince the bioavailability of most natural ingredients is low, an important application of DPI is to improve this problem. DPI is administered directly through the lungs without passing through the enterohepatic circulation, avoiding the first-pass effect and allowing increased entry into the systemic circulation, thus improving bioavailability. Lu et al.  preparedIn dry powder inhalers, the drug is stored as a dry solid powder; the drug is less susceptible to environmental disturbances during production, transport, and storage; the molecular activity is low; the drug is protected from oxidation and decomposition; and its physical and chemical stability is improved. In addition, stabilizers are usually introduced in the formulation design to ensure that the active ingredients of the drug are not destroyed. One study  found thDry powder inhalers for inhalation of various natural ingredients are described in In contemporary drug formulation design, it is very important to find the appropriate active ingredients, dosing route, and production process for the reasonable application of natural foods with medicinal value. Many natural resources are of rich medicinal value, and natural ingredients are often used as the main raw materials for drugs. However, their unstable chemical properties, low solubility, and low systematic bioavailability often hinder their development and application. Therefore, there is an urgent need to find suitable preparation methods and application means. In this paper, the principle and synthesis of spray drying technology in the preparation of dry powder inhalers of natural products are reviewed, and some successful applications are analyzed. In a word, the use of spray drying technology to produce drugs containing natural ingredients has great prospects and provides the possibility for more drug applications of natural products. In the future, with the increasing incidence of lung diseases such as lung cancer, pneumonia, chronic obstructive pulmonary disease, and pulmonary fibrosis, preparations for pulmonary administration, especially dry powder inhalers, will have great research significance and market prospects. Especially with natural active products, their reasonable and scientific use is also a major breakthrough in pharmaceutical research. Therefore, the research and application of dry powder inhalants with natural active products are important for human health and scientific development.BW: writing—original draft preparation. JX, ST, and WZ: writing—review and editing. ST and BH: supervision and approval. All authors contributed significantly to the writing of the manuscript.


The collection of family health history (FHH) is an essential component of clinical practice and an important piece of data for patient risk assessment. However, family history data have generally been limited to diseases and have not included medication history. Family history was a key component of early pharmacogenetic research, confirming the role of genes in drug response. With the substantial number of known pharmacogenes, many affecting response to commonly prescribed medications, and the availability of clinical pharmacogenetic (PGx) tests and guidelines for interpretation, the collection of family medication history can inform testing decisions. This paper explores the roots of family-based pharmacogenetic studies to confirm the role of genes in these complex phenotypes and the benefits and challenges of collecting family medication history as part of family health history intake. The use of family health history (FHH) data has experienced a resurgence of sorts with the development of electronic medical records, clinical decision supports, and patient portals facilitating the collection and utilization of this valuable information . Alone oAs today’s FHH intake primarily focuses on diseases and risk assessment, other health outcomes such as drug response have not received the same recognition but may benefit from consideration within the family health history intake process. Historically, the study of families was instrumental to deciphering the role of genes in drug response, which, in some instances, informed treatment decisions. While adverse responses to foods and medicines have been documented for centuries, the recognition of the importance of FHH was demonstrated by Sir Archibald Garrod and his study of families with inherited biochemical disorders. His prescient work in biochemical genetics also recognized individuals’ different responses to pharmacological treatment . With discoveries of Mendelian-like adverse drug responses in the mid-1900s, FHH continued to be a key part of studies to ascertain the role of genetic factors. For example, the early use of isoniazid in tuberculosis patients demonstrated a bimodal pattern of metabolism . Multiple family studies soon confirmed the genetic cause of the slow/rapid rates of isoniazid metabolism. Knight et al. (1959) published their analysis of 91 individuals from 20 families, concluding an autosomal recessive inheritance pattern [At about the same time, other researchers were studying the enzyme pseudocholinesterase. In 1956, Lehmann and Ryan  studied In yet another example in 1960, a case report was published of a young male patient in Australia who was scheduled to undergo surgery for a compound fracture of the tibia and fibula . The patToday, the clinical use of whole genome and whole exome sequencing continues to expand for disease diagnosis and risk assessment, yet its role and value, alone or in combination with FHH, has not been unequivocally demonstrated. In a study of 1750 healthy Singaporeans that had both a high-quality FHH and whole genome sequencing, FHH information defined the cancer-related clinical care strategy in 98% of participants . SpecifiDespite the well-documented role of inheritance of adverse events for select drugs, family medication history is not typically included as part of the standard FHH intake. In 2016, Smith et al. suggested that family medication history be collected and utilized to inform decisions about PGx testing . MedicatDespite its potential, establishing broader PGx testing continues to be limited. A family medication history may indicate a need for PGx testing to confirm an increased risk of adverse events or non-response to certain medications. Family history of Stevens-Johnson syndrome  can infoThere are some specialties and clinical scenarios in which medication history is typically collected. For example, psychiatrists collect a medication history during a new patient visit to understand their prior drug experiences. This information can then be used to inform future treatment decisions, with the goal of avoiding both ineffective medications and adverse events ,39. OtheSome challenges should be considered when expanding FHH to include family medication history. It has been well-documented that FHH is under-utilized for a variety of reasons ,43,44,45Discrepancies between self-reported medication history and prescription history in the medical record have been demonstrated ,64, highThere has been limited evaluation of the possible solutions that could enhance uptake and accuracy of FHH and medication history. For FHH, several digital-based tools have been developed and evaluated to promote FHH collection and analysis ,70,71,72For medication history, many digital applications have been developed to improve medication adherence, though the quality and effectiveness are debatable . These sLastly, the collection and review of additional information will likely add to the provider’s consultation time per patient, and potentially the need for more healthcare professionals, especially pharmacists and multidisciplinary teams ,79,80. DAs with any medical condition, predicting response to medications will be impacted by awareness of multiple factors, some yet to be determined. The combination of FHH and medication history, potentially with PGx test results, may yield a better predictor for medication use and improved health outcomes. As new FHH tools and resources continue to be developed and updated, these may facilitate reporting of family medication usage and responses. Thus, it may be worthwhile to consider the inclusion of adding family medication history to routine patient intake processes to identify patients at increased susceptibility for an adverse response. Further research is needed to demonstrate the utility of an enhanced FHH to include medication history and strategies to facilitate accurate and complete patient reporting. Greater attention to patient and family history of medications may indirectly promote awareness, medication adherence, and recognition and reporting of adverse events.


Multi-omics profiling has been used to reveal molecular signals representing the metabolic and functional diversity of microbiomes in the gut microenvironment (Park et al., bifidobacteria play a leading role early in life, followed by abundant Prevotella. Moreover, to better understand the diversity of metabolic activities of B. longum clades, they also found that B. longum clades and numerous secondary metabolites were associated with diarrhea and early growth in children, while transitional B. longum was correlated with the metabolites derived from microorganisms, including citrulline, imidazole propionic acid, N-acetylputrescine, and 5-hydroxyectoine. This work provided evidence for the importance of B. longum subsp. Infantis and its associated metabolites in the host-microbe symbiosis and the development of the infant.A recent study by Vatanen et al. investigated the maternal microbiome shaping the gut microbial metabolism, consisting of mother-infant samples at different stages of infancy and late pregnancy (Vatanen et al., Cell, Park et al. reported that butyrate derived from microorganisms is an environment-dependent inhibitor of the Bacteroidales and revealed its key role in the gut microbiota microenvironment (Park et al., Bacteroides is linked to, but not independent of, the expression of acyl-CoA transferase and butyrate. It is noteworthy that the inhibitory mechanism of butyrate is related to variations in the metabolic function and regulation of acyl-CoA. Park and colleagues determined that butyrate, a specific microbial metabolite, has an inhibitory effect on Bacteroides and then demonstrated that acyl-CoA metabolism plays a role in metabolic defense. By identifying the specific inhibitory function of butyrate, this work proposed a relationship between growth inhibition and metabolism. The active metabolites produced by the gut microbiota and their interactions with surrounding environmental signaling molecules play a major role to regulating human health in various ways, affecting genetic, biochemical, and biological functions to maintain host metabolic homeostasis. The application of gut microbiota in regulating health benefits through metabolomics is gradually expanding. Based on second-trimester vaginal and pregnancy samples, Kindschuh et al. found multiple associations between metabolome and microbiome, including the association between bacteria and metabolites enriched in term pregnancies (Kindschuh et al., Microbial communities can produce various metabolic products to regulate host physiology and disease, but their mechanisms of action are not fully understood. In a recent study in Taken together, these multi-omics studies provide excellent examples of revealing the functional metabolic diversity of microbiomes by generating cross-talk between microbiome and host. They also emphasize the effects of gut microbiota and small-molecule metabolites on pathophysiological roles related to health and disease. The gut microbiota can be considered the largest immune organ and is highly associated with systemic metabolic disorders, so targeting the microbiota may be a potential therapy for metabolic diseases. Nevertheless, these studies have paid less attention to diet and lifestyle changes that may have affected the microbiome and metabolism. Further research is needed to explore the complex microorganism-host interactions that may affect the occurrence and development of disease. Additionally, the molecular mechanisms of the metabolic functional diversity of microbiomes in the gut microenvironment remain to be determined. The upcoming expansion of the endogenous metabolite database will provide analytical opportunities to discover the relationship between microbial metabolites and the host. Artificial intelligence, machine learning, multidimensional statistical methods, and accessible analytical tools for discovering the association of microbial metabolites and metabolic functional diversity are crucial for analyzing the large amount of data generated by gut microflora and microbiome research in the gut microenvironment.SQ, ZW, QY, and YC participated in the study design, contributed to method development, and wrote the manuscript. YX, ST, and AZ provided advice on method development and analysis and contributed to drafting the manuscript. All authors contributed to the article and approved the submitted version.


Ginger is an herbaceous and flowering plant renowned for its rhizome, which is widely employed as both a spice and an herb. Since ancient times, ginger has been consumed in folk medicine and traditional cuisines for its favorable health effects. Different in vitro and in vivo studies have disclosed the advantageous physiological aspects of ginger, primarily due to its antioxidant, anti-inflammatory, antimicrobial, and anti-carcinogenic properties. These health-promoting features are linked to the variety of bioactive compounds that are present in ginger. Following the advancement in consumer awareness and the industrial demand for organic antioxidants and functional ingredients, the application of ginger and its derivatives has been broadly investigated in a wide range of food products. The prominent features transmitted by ginger into different food areas are antioxidant and nutraceutical values (bakery); flavor, acceptability, and techno-functional characteristics (dairy); hedonic and antimicrobial properties (beverages); oxidative stability, tenderization, and sensorial attributes (meat); and shelf life and sensorial properties . This review is focused on providing a comprehensive overview of the tendencies in the application of ginger and its derivatives in the food industry and concurrently briefly discusses the beneficial aspects and processing of ginger. Zingiber officinale Roscoe) plant, producing an irregular bumpy rhizome, is extensively cultivated in the southeast region of Asia. Accurate information regarding ginger plant’s origin is not available due to a very long cultivation history in this region. In ancient Greek history (40–90 AD), the Greeks were familiar to the dietary and culinary uses of ginger. In the 13th century, during his visit to China and Sumatra, Marco Polo became acquainted with ginger and imported it to Europe. In the same era, the Arabs transported the ginger from India to East Africa, while the Portuguese spread it to West Africa . Fo. Fo27]. Gingerols are sensitive to some processing and storage conditions like thermal processing, acidic environments, air, light, and prolonged storage. When gingerols undergo these conditions, there is a possibility of the transformation of gingerols into shogaols . StudiesThe chemical composition and yield of ginger extract and ginger essential oil are also influenced by the extraction method. Advanced and green extraction techniques are preferred over classical extraction techniques . The extFurthermore, a synergistic effect with a superior quality product could also be achieved through the consolidation of these extraction techniques, and this is the most emerging approach in the field of extraction to maximize the extraction efficiency through the synchronization of two or more advanced extraction technologies . For insAs mentioned above, the functional and bioactive compounds present in ginger are associated with multiple health benefits and advantageous characteristics. The exploitation and characterization of the versatile biological and functional competencies of ginger are also crucial for further research. These multifaceted bioactivities of ginger bioactives and their modes of action are discussed here in this section.Any disturbance or adjustment with the cell’s antioxidant defense system can lead to the excessive production of Reactive Oxygen Species (ROS), and eventually, to an increase in oxidative stress. For the recovery of normal levels of free radicals and oxidative stress, various natural antioxidants have been analyzed for the neutralization of free radicals and encounter oxidative stress for the prevention of different health disorders . The maiIn the past two decades, the exploration of natural plant resources has become very attractive and beneficial, most probably due to the presence of bioactives, less toxicity, and the lower production costs . Ginger Inflammation is a phase in the natural healing process of the body to combat intruders for the prevention of infection or injury. The basic aim of inflammation is to abrogate cellular damage, withdraw and absorb necrotic cells and tissues, and regain the stability and conformity of the intracellular environment . When ceIt was also observed that the anti-neuroinflammatory capacity of gingerols enhanced with the increase in the alkyl chain length as 10-gingerol’s anti-neuroinflammatory activity was higher than the other active compounds of ginger . FurtherPsedomonas aeruginosa (a multi-drug-resistant strain) by disturbing the membrane stability and preventing biofilm formation. Currently, chemical and synthetic antimicrobials are being replaced with natural and organic antimicrobials derived from plants, animals, and microorganisms, both in health management and in food preservation. These biologically derived antimicrobials from plant sources are preferred due to their multi-oriental management, multi-targeted approach, drug resistance, and lower toxicity . Ginger Escherichia coli and Staphylococcus aureus. A study reported the bactericidal mechanism of ginger essential oil through the demolishment of the cell membrane activity and through intervening with the energy metabolism because of some macromolecular elements like proteins and nucleic acids [Candida, Aspergillus niger, and P. aeruginosa and a mild effect against Saccharomyces cerevisiae, and no inhibition was detected against Bacillus subtilis, Trichoderma spp., and Pencillium spp. Ginger oil obtained from dried ginger was observed to have higher anti-bacterial activity against P. aeruginosa [Ginger essential oil possesses excellent anti-bacterial activity against food-borne pathogenic bacteria such as ic acids . Anotherruginosa . There iruginosa .Cancer is one of the major causes of death in the world, and in 2020, approximately 19 million cancer cases were reported, out of which about 10 million cases worldwide were fatal . In receIn vivo and in vitro studies were carried out to evaluate the action mechanism and cytotoxic effects of ginger against prostate cancer . It was There is a possibility of an onset of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease, and old people are at an especially high risk. Many recent studies have expressed that ginger possesses anti-neuroinflammatory capacity and has a significant role in memory function, which might be useful in the management and inhibition of neurodegenerative diseases ,63. It wWorldwide, the dominant cause of premature deaths (17.9 million people per year) is cardiovascular disease . VariousObesity or overweight is a great concern throughout the world, and it emerges from the disturbance in the energy metabolism. Basically, there are two types of adipose (fat) tissues classified on the basis of their arrangements and activities. The core function of white adipose tissues is to serve as energy depositories, while brown adipose tissues are responsible for metabolism improvement and energy expenditure. The transformation of white adipose tissues to brown adipose tissues is a novel approach to tackle obesity. 6-Gingerol could stimulate the cell browning via an AMPK-dependent approach in 3T3-L1 adipocytes ,69. GingDiabetes mellitus is a chronic metabolic disease characterized by the insufficiency of insulin and/or insulin resistance, resulting in an uncontrolled increase in the blood glucose level. Persistent hyperglycemia could promote protein glycation and the generation of advanced glycation end products (AGEs). An in vitro study revealed that 6-gingerol and 6-shogaol inhibited the proliferation of diabetic complexities and confined the methylglycoxal (MGO), the precursor of AGEs, to hinder AGE production . The oraCurrently, there is a growing trend towards healthier and functional food products as consumers are more conscious about healthy lifestyles and daily intakes. Due to prospering recognition and interest in functional food products, the scope for the exploration, characterization, and potential utilization of functional food ingredients and bioactive compounds has also magnified. Ginger, being a valuable functional ingredient and marvelous source of bioactives, has also been integrated into a variety of food products. Here, the food applications of ginger in its different forms  will be discussed to provide a comprehensive and broader prospective of its employment. The impact of ginger incorporation on the sensorial attributes of various food products is also discussed below .Rhizopus stolonifer on bread, but ginger may impart unfavorable flavor characteristics to the bread [A variety of studies have been carried out to use ginger as a potential food additive in bakery products . For inshe bread . The aforementioned studies mainly dealt with the addition of ginger to produce functional bread. However, in some parts of the world, distinct baking products exist wherein ginger is used as a dominant flavoring ingredient. For instance, gingerbread is a traditional bakery product in the Czech Republic, particularly in the town of Pardubice, where ginger is the prevalent flavor. It is produced in a variety of shapes, types, and textures ranging from a crispy biscuit to a soft cake ,80. HencLee et al.  proposedThe functional, nutritional, antioxidant, and sensorial properties of millet flour and ginger powder-formulated cookies were investigated by Marak et al. . This stTo a large extent, Almasodi  successfAs per the foregoing studies, ginger powder is the most widely employed form of ginger in bakery products, followed by ginger juice. The pertinent amount of ginger powder and processing conditions are the key factors in the acquirement of balanced functional bakery items without any alteration in the desirable characteristics of the product. So, bakery products with admirable nutraceutical values could also be produced through the precise supplementation of ginger. There is also a considerable potential for innovation and improvement in the dairy sector, where ginger, an active ingredient, has also contributed . The supA novel approach was also attempted to evaluate the effects of ginger, cinnamon, and carob powders on spray-dried milk powders and their agglomerates . Milk pov/v) was regarded as the suitable concentration in yogurt production [Apart from ginger powder, the effect of ginger juice on the physicochemical and hedonic attributes of yogurt was also investigated at different concentrations. An inverse relationship was observed among the amount of ginger juice, the viability of the starter culture, and the hardness of the product. Based on the finding of this study and the sensorial characteristics of the product, the ginger juice addition at the rate of 4% . The increase in the ginger juice concentration was inversely proportional to the fat, protein, and total solid contents of the product. Considering the sensorial properties and other attributes, a 2.5% ginger juice addition in the milkshake was regarded as an optimum value. Gaur et al. [Jadhav et al.  formulatr et al.  also sucw/w) with a better hedonic response than all other treatments including the control. Ginger juice improved the product’s resistance against melting but lowered the fat and total solid contents in the herbal ice cream [L. acidophilus-based probiotic culture to produce probiotic ice cream at the optimized levels of 3%, 6%, and 7% (w/w), respectively.Several studies were also designed for the potential exploitation of ginger into an herbal ice cream product. Herbal ice cream could be manufactured employing 4% ginger juice  and goat milk was characterized with the highest antioxidant activity than other milk sources . A sensorial analysis revealed that herbal yogurt augmented with ginger extract (1%) was superior in color and appearance, while garlic extract (0.2%) was responsible for a better taste and flavor ,107.Ginger extract in combination with gum arabic has also been exploited in the manufacturing of a yogurt-based traditional Iranian drink, Doogh . The synThe ethical, economical, and supply chain aspects of the calf rennet compelled dairy experts to explore other potential sources of milk coagulation. Ginger and its derivatives were also analyzed as potential coagulants in cheese processing. Ginger crude extract emerged as an auspicious coagulant in soft un-ripened cheese manufactured from camel milk . Ginger Ginger extract has been productively employed in soft cheese manufacturing as a functional ingredient. Ginger extract addition promoted the quality attributes of soft cheese by reducing the pH, oxidative rancidity, and microbial load while increasing the proteolysis, cohesiveness, and smoothness of the product. Ginger extract-fortified cheese also earned higher scores for texture, flavor, and overall acceptability than the control. Ginger extract (4%) coupled with the pickling of the soft cheese was reported with superior quality characteristics . Herbal ice cream with health-promoting attributes and a reduced sugar content could also be prepared by formulating ginger extract and xylitol . The phyIn another study, different forms of ginger such as ginger juice, paste, candy, and powder were formulated into ice cream to analyze its physicochemical and hedonic attributes. The addition of ginger juice and paste decreased the fat, total solid, and overrun contents, while the incorporation of ginger candy and powder increased the fat, total solid, and overrun contents, but the phenolic contents and antioxidant capacity were increased in all inclusions. The product’s resistance to melting improved in all preparations of the ginger, and the optimized incorporation level of all ginger forms were 4% paste, 6% juice, 10% candy, and 1% powder . A novel approach of utilizing ginger essential oil in yogurt preparation was also successfully applied to evaluate the quality characteristics of the final product. Ginger essential oil enrichment (1000 ppm) lowered the syneresis and pH, while it enhanced the shelf life of the product and improved the overall acceptance of the product . EffortsIn conclusion, ginger’s diversified applications in dairy products have demonstrated its vast potential as a functional ingredient. Among the different forms of ginger, ginger extract came up as an extensively employed form, followed by ginger juice, powder, and essential oil. Ginger extract exhibited its aptitude for improving the overall techno-functional characteristics of dairy products and offering persuasive coagulation properties for cheese manufacturing. While the use of all forms of ginger proved to be effective in elevating the functionality and efficacy of dairy products, at the same time, some quality and hedonic characteristics were also compromised, but the incorporation of ginger extract was identified with higher functional and physicochemical improvements and minimal quality defects.Like other food sectors, the incorporation of functional food ingredients including ginger has also been investigated in beverages . In the Ginger extract in combination with lemon juice rendered an excellent job as a natural preservative in a carrot–mandarin RTS drink. During storage, a few modifications were observed in the physicochemical properties, but the product characteristics remained consistent to a large extent . A gingeFresh ginger extract was adequately exploited in the production of a ginger-based functional carbonated beverage. The functionality and sensorial characteristics of the drink were improved with the addition of ginger rhizome extract, but the shelf life was largely associated with the carbonation level of the product . In anotHibiscus sabdariffa flowers [Ginger is also a vital ingredient in Zobo drink, a traditional and indigenous Nigerian drink prepared from  flowers . The for flowers . v/v) was furnished for a functional beverage utilizing ginger extract (15.1%), tamarind extract (9.9%), turmeric extract (5.0%), sugar solution (40%), and water (30%). Ginger has also been formulated in various functional beverages in the form of blends with different spices, herbs, fruits, and other food ingredients. A soybean, ginger, and mango-based functional drink was developed to effectively exploit the nutritional and functional values of these ingredients, which were consequently attributed with feasible sensorial properties and overall acceptability . An optiGinger extract, Kencur extract, and their combination blend were incorporated into an instant temulawak beverage, an indigenous Indonesian drink, to evaluate their impacts on the physicochemical and sensorial attributes of the final product. The inclusion of ginger extract in a concentration of up to 15% enhanced the flavor, aroma, and overall acceptance of the Temulawak drink . AnotherThe integration of two different food ingredients could ameliorate the defective quality attributes of the intended food product. The fusion of a better coloring feature from tea and a favorable flavoring attribute from ginger resulted in a superior acceptable beverage product . During The above-mentioned research findings have provided insight into the exploitation of ginger in beverage products. Even in beverages, the employment of ginger extract seems to dominate over the rest of the ginger derivatives due to its better assimilation and conducive characteristics. The incorporation of ginger into different drinks not only augmented but also maintained the shelf life, nutraceutical value, and antimicrobial activity. Several investigations and experiments were also conducted to figure out the effects of ginger and its derivatives in various forms on meat and fish products . Rabbit A comparative analysis was conducted to assess the effects of three different spices as potential organic preservatives in raw chicken meat products. Clove powder (0.2%), even in a very small quantity, compared to ginger (3%) and garlic (2%) pastes performed very well to improve the oxidative stability, physicochemical properties, and antimicrobial activities in raw chicken meat with refrigerated storage . Ginger It was revealed that the competence level of ginger was as good as sodium ascorbate to combat oxidative rancidity, while the textural and sensorial properties of meat products with ginger were better than synthetic antioxidants . Ginger Smoked mackerel fish was treated with ginger extract to evaluate its influence on the shelf life and hedonic characteristics . Ginger A variety of synthetic tenderizers and proteolytic enzymes had been engaged in the tenderization of meat. Currently, natural tenderizers are in demand due to certain constraints of synthetic tenderizers such as safety issues, unpleasant tastes, and over-tenderization. Ginger extract (5%) was recommended as a potential tenderizer for Yak meat as it enhanced the cooking yield, tenderness, and lipid oxidative stability during storage ,150. Researchers had also adopted a distinct approach to improve the meat quality through feeding ginger, as a potential dietary supplement, to the animals. Ginger and its derivatives in various forms were highly effective in inducing favorable characteristics such as lowering the cholesterol and improving the egg functionality and carcass quality in poultry animals ,152. TheAs per the aforementioned studies, ginger has an extensive application potential in the meat industry, embellishing meat products with an array of favorable characteristics. The incorporation of ginger into meat products competently ameliorated the nutritional value, oxidative stability, lipid profile, and antimicrobial activity of the meat products. The ability of ginger to provide elegant antioxidant and tenderization activities without compromising the sensorial and textural attributes of meat products has also provided another priority lead over artificial antioxidants and tenderizing agents. The inclusion of ginger in the diets of animals as a dietary supplement has also emerged as a novel and efficient approach, which remarkably improves the fatty acid profile and carcass quality of the animals.Ginger and its bioactive compounds have also been examined for their potential applications as edible coatings, films, or active ingredients in packaging . The usev/v) ginger essential oil, regarded as whey protein isolate ginger (WPIG) coating [E. coli and S. aureus, while Tsironi et al. [Whey protein isolate (WPI) coating on Kashar cheese was characterized with water and fat barrier attributes, which were significantly developed with the addition of 1.5%  coating enhanced the shelf life and maintained the quality attributes of beef; moreover, these characteristics were substantially improved with the incorporation of ginger essential oil (SAG) into edible coating . CompareFish sarcoplasmic protein and chitosan composite films enriched with ginger essential oil (up to 3%) were analyzed for their physicochemical and antioxidant features . Ginger Zhang et al.  also forIt is evident from the foregoing literature that ginger oleoresins have promising applications in edible films, coatings, and the packaging sector, where ginger essential oil has better and extensive utilizations than ginger extract. The consolidation of ginger significantly improved the oxidative stabilities, antimicrobial activities, and sensorial properties of the intended food products. Ginger also performed better than the synthetic preservatives, improved the shelf life, and retained the quality attributes of the products.Ginger, with strong antioxidant, antimicrobial, and other functional activities, has a great potential for employment in the food industry. In this review, many studies have been presented regarding the acquirement of the bioactive compounds of ginger in different forms such as powder, extract, juice, paste, and essential oil. The application of ginger and its derivatives in a variety of food products has been extensively investigated and reported with auspicious results. Among all of the derivatives of ginger, ginger extract was found to be a more practicable, efficient, and versatile form of ginger for food applications. Ginger essential oil is another efficient variant of ginger, but its food applications are limited because of its strong aroma and immiscibility with certain food products.These studies have also revealed another positive aspect of ginger, which is that it could be employed in a wide range of food products, covering almost all leading sectors of the food industry. The incorporation of ginger, in its various forms, thoroughly improved the functionality and bioactivity of the intended food products, but the physicochemical and sensory properties of these products were also compromised in a legitimate number of studies. Further studies mainly focused on the efficacy, optimization, and assimilation of ginger bioactives into food products without compromising the physicochemical characteristics and sensory profiles of the products must be carried out. The probable synergistic effect of ginger bioactives with other compounds and toxicity studies of different compounds in their purest forms should also be investigated to ensure the safe use of these functional compounds.