The identification of drug/compound–target interactions (DTIs) constitutes the basis of drug discovery, for which computational predictive approaches have been developed. As a relatively new data-driven paradigm, proteochemometric (PCM) modeling utilizes both protein and compound properties as a pair at the input level and processes them via statistical/machine learning. The representation of input samples  in the form of quantitative feature vectors is crucial for the extraction of interaction-related properties during the artificial learning and subsequent prediction of DTIs. Lately, the representation learning approach, in which input samples are automatically featurized via training and applying a machine/deep learning model, has been utilized in biomedical sciences. In this study, we performed a comprehensive investigation of different computational approaches/techniques for protein featurization , data preparation and exploration, machine learning-based modeling, and performance evaluation with the aim of achieving better data representations and more successful learning in DTI prediction. For this, we first constructed realistic and challenging benchmark datasets on small, medium, and large scales to be used as reliable gold standards for specific DTI modeling tasks. We developed and applied a network analysis-based splitting strategy to divide datasets into structurally different training and test folds. Using these datasets together with various featurization methods, we trained and tested DTI prediction models and evaluated their performance from different angles. Our main findings can be summarized under 3 items: (i) random splitting of datasets into train and test folds leads to near-complete data memorization and produce highly over-optimistic results, as a result, should be avoided, (ii) learned protein sequence embeddings work well in DTI prediction and offer high potential, despite interaction-related properties  of proteins are unused during their self-supervised model training, and (iii) during the learning process, PCM models tend to rely heavily on compound features while partially ignoring protein features, primarily due to the inherent bias in DTI data, indicating the requirement for new and unbiased datasets. We hope this study will aid researchers in designing robust and high-performing data-driven DTI prediction systems that have real-world translational value in drug discovery.The online version contains supplementary material available at 10.1186/s13321-023-00689-w. Drug discovery is a long-term and costly process that involves the identification of bioactive compounds as drug candidates via screening experiments. Although the advancements in high-throughput screening technology allow the scanning of thousands of compounds simultaneously, it is still not possible to fully analyze a certain portion of the target and compound spaces due to the excessive number of possible protein-compound combinations. This situation led to the emergence of computational approaches, such as the virtual screening (VS), for the in silico prediction of unknown drug–target interactions (DTIs) to aid screening experiments . ConventUntil recently, most machine learning-based computational drug discovery studies approached the subject of predicting physical interactions between drug candidate compounds and target proteins from a (ligand-based) chemo-centric point of view, only utilizing compound attributes/properties. These studies ignore protein features by treating target proteins just as labels for input compounds. As a rather novel approach in this area, proteochemometric (PCM) modelling aims to predict bioactivities by incorporating both compound and target features, usually via readily available molecular notation  and amino acid sequence data, without requiring hard to obtain 3-D structures and dynamic information . PCM canVarious types of featurization approaches have been used for representing compounds and proteins. Due to the abundance of ligand-based DTI prediction methods, compound representations are extensively studied in the literature –9. ThereFor evaluating the effectiveness of different types of protein featurization in different areas of protein informatics, carefully designed benchmark studies are required. In contrary to studies that investigate compound featurization, only a few works are available for benchmarking protein representations. These studies mostly focus on tasks such as protein family prediction , bioactiPCM modelling has shown promising results when compared to conventional approaches of DTI prediction , 35; howNew computational approaches, evaluation strategies and datasets are required in order to address the aforementioned issues in data-centric evaluation and prediction of DTIs. With the aim of contributing to the field of data-driven bioactivity modelling for drug discovery and repurposing, here, we performed a rigorous benchmarking study. One of the goals in this study is to identify feature types with better representation capabilities to be used in the automated prediction of DTIs. To achieve this, we built prediction models for various sequence-based protein representations. We employed widely used conventional protein descriptors by selecting those that reflect different molecular aspects of proteins. We also utilized the state-of-the-art protein representation learning methods . Another goal of this study is the preparation of new challenging benchmark datasets with high coverage on both compound and protein spaces, which can also be utilized in future studies. We carefully prepared small-, medium- and large-scale datasets by applying extensive filtering operations and a network-based splitting strategy to acquire realistic and well-balanced datasets. To our knowledge, this data splitting strategy which considers 3 types of relationships , is proposed here for the first time. We used these datasets in our protein representation benchmarks. In this study, we also evaluated different forms of; (i) DTI modeling techniques, (ii) preliminary and explanatory data exploration approaches, and (iii) model performance evaluation and comparison strategies.https://github.com/HUBioDataLab/ProtBENCH.The study is summarized in a schematic workflow in Fig. As the first comprehensive benchmark study including both conventional and novel protein representation methods in the context of drug discovery and repurposing, we hope this work will aid researchers in choosing suitable approaches and techniques according to their specific modeling tasks. Furthermore, our newly constructed challenging benchmark datasets can be used as reliable, reference/gold-standard datasets in further studies to design robust DTI prediction models with real-world translational value.In this section, we evaluate and discuss the results of our benchmark experiments. For this, we first carried out a data exploration analysis. Next, we trained DTI prediction models under different settings and measured their performance. At each subsection, we discussed our findings from various aspects to address shortcomings in bioactivity modelling studies.Here, we employed random forest (RF) as our main machine learning algorithm  for predicting DTIs. The reasons behind using a classical machine learning algorithm in this benchmark study rather than more complex deep learning-based architectures is that: (i) RF has been used in this field for a long while and shown to work well on numerous occasions, (ii) deep learning-based complex architectures have already been used in the training stage of learned representations ; thus, the use of additional complex architecture in the supervised DTI prediction stage could have prevented the observation of the ability of learned representations in extracting ligand interaction-related properties of proteins, and also, hinder the evaluation of model-driven  and data-driven  approaches on common ground, and (iii) hyperparameter value selection have a significant effect on the performance of deep learning models. If we had used deep learning models in this benchmark study, the model performances would have been heavily influenced by the specific hyperparameter settings used, and any differences in performances could not be attributed solely to the representation capabilities of the featurization approaches. In this study, the main aim is to fairly compare and evaluate different representation approaches rather than constructing a single DTI prediction model with maximized performance. As a result, we used classical machine learning algorithms, which do not require the same level of hyperparameter tuning as their deep learning-based counterparts.In this subsection, we first visualized members of protein family-specific datasets on 2-D via t-distributed stochastic neighbor embedding (t-SNE) projection. Then, we analyzed split-based characteristics of our datasets by plotting pairwise similarity distributions of proteins and compounds, bioactivity distributions of train-test folds, together with their respective t-SNE embeddings.For each protein representation, two independent t-SNE projections  were carried out Fig. a, b. Prosimsearch function of the Chemfp python package [To explore protein and compound diversity in our datasets, we evaluated protein–protein and compound-compound pairwise similarities of the members of a selected representative protein family , in terms of “train vs. train”, “test vs. test”, and “train vs. test” comparisons for each split strategy . For this, we aligned protein sequences using EMBOSS Needle global pairwise sequence alignment tool  and plot package . Since iFigure Most of the traditional machine learning algorithms such as RF and SVM operate on the independent and identically distributed data (IID) assumption for the samples in training and test splits. In other words, the values of the variables in a dataset are assumed to be independent of each other and have the same probability distribution. This assumption may be violated if there is a shift in the distribution of the input or output variables between train/test splits, which may affect the performance of the model . TherefoTo explore the IID assumption in terms of output variables , we plotted bioactivity distributions of protein family-specific datasets based on train-test samples of each split. Figure In cases of the presence of a shift in output variables, models require extrapolating beyond the minimum and maximum target values in the training datasets. This may be a limiting factor for regression-based algorithms that can only generate predictions within the boundaries of training output values . Therefop-values. Additional file p-value < 0.01) shift between the KS distance value distributions of train and test samples for 19 (for fully-dissimilar-split) and 7 (for dissimilar-compound-split) features out of the total 80 features in apaac, and 558 (for fully-dissimilar-split) and 189 (for dissimilar-compound-split) features out of the total 768 transformer-avg features; whereas, none of the variables were significantly shifted in the random-split dataset, considering both representations.We also compared the distributions of protein representations and ecfp4 compound fingerprints in-between training and test splits to check the IID assumption for input variables. For protein representations with continuous values, we applied Kolmogorov–Smirnov (KS) test and calculated KS distance scores for each feature  of train and test samples along with corresponding p-values. Therefore, 743 and 689 of a total of 1024 compound fingerprints were significantly shifted on the fully-dissimilar and dissimilar-compound splits, respectively, whereas this number was 47 for the random split. For significance, we accepted a p-value < 0.001 since the chi-square test is sensitive to sample size, which has the risk of falsely defining significant relationships in the presence of a large sample size, as in our case.For compound fingerprints, we applied the chi-square test, since they are composed of binary variables rather than continuous ones. We didn’t plot the score distributions of the chi-square test since it doesn't provide a direct distance measure. Instead, we evaluated these shifts based on their The observation of a shift between the KS distance score distributions of models trained on fully-dissimilar and dissimilar-compound splits was not surprising since this is a common issue in real-world drug discovery applications, where the general aim is to seek completely novel small molecules that are bioactive against the targets of interest. It is also one of the reasons why most of the models, well-performing on “easy” datasets , start to fail in realistic scenarios. It is possible to mitigate the shifting problem by applying preprocessing techniques such as feature dropping or importance weighting , especiaIn this analysis, we visualized the distribution of bioactivity data points  on 2-D via t-SNE projections to observe how train and test fold samples are separated from each other under different splitting settings. For each protein family-based dataset, 1500 data points were randomly selected (from both train and test folds), since the number of training samples dominates test samples in the original datasets. Each bioactivity data point was represented by the concatenation of its protein and compound feature vectors, and used as input to the t-SNE algorithm.In Fig. There are numerous conventional descriptor sets for proteins in the literature, most of which can be utilized for DTI prediction. Evaluating all descriptor sets on our large-scale datasets would not be feasible considering the computational cost; as a result, we decided to carry out a small-scale analysis to pre-select the descriptor sets that are successful in DTI prediction, and use the selected descriptors in both the medium-scale and large-scale analysis later. Additionally, it was required to determine the supervised learning algorithm to be used for DTI prediction in this study, and due to, again, the computational complexity related issues, we decided to make a performance comparison (between SVM and RF) on these small-scale datasets.In this analysis, we assessed the success of SVM- and RF-based DTI prediction models, each utilizing one of the 42 conventional protein descriptor sets and a baseline . The full list of descriptor sets is provided in Additional file Figure At the end of this analysis, we decided to continue with RF, to be used throughout the study. Also, we selected 10 conventional descriptor sets with both high and low performances, and distinct properties regarding the protein features they incorporated and used them in the following benchmarks . Here, instead of simply selecting the best-performing descriptors, we sought a diverse set of descriptors that are constructed using different types of information . Another criterion was that the selected descriptors should not have similar performance scores . Therefore, rather than comparing similar approaches with a high probability of yielding similar results on medium- and large-scale analyses, we attempted to acquire a representative set of descriptors, each of which has the potential to reveal a different characteristic presented in target protein sequences.PCM modelling approach can handle high numbers of training instances, belonging to different compounds and proteins, within a single predictive model, in contrast to ligand- and target feature-based modelling which requires the generation of separate models for each protein or compound (or compound cluster), respectively. Thus, PCM modeling brings the advantage of learning from larger datasets, which is a critical requirement in machine learning, in general. Another advantage of PCM modeling is the joint utilization of compound and protein features to better model their interaction-related properties, without the requirement of scarce and difficult to analyze 3-D structural information, unlike target-based structure modelling approaches. In the following benchmarks, we aimed to evaluate protein representations in terms of PCM modeling, over the problem of regression-based DTI prediction. Here, we constructed PCM models for 10 selected conventional protein descriptor sets, 6 learned protein embeddings and 2 baseline models , Spearman rank correlation, MCC and F1-score  are given in Fig. Also, there is an overall increase in MCC scores of conventional descriptor sets (excluding dde and spmap) when we compare the results of small- and medium-scale analyses. In addition to the contribution of the increased sample size, this situation can be associated with the involvement of compound features in PCM-based models, which probably led to a better learning over the joint protein–compound interaction space. On the other hand, PCM models here had lower F1 scores than the target feature-based models in the small-scale analysis. In order to calculate MCC and F1-scores for PCM models, we converted real-valued predictions into binary format at the cut-off value pKd = 7, which is also used in other studies as a bioactivity threshold for kinase inhibitors . HoweverFinally, the baseline models displayed the lowest performances in this analysis, similar to the results of the target feature-based modelling experiment.The main goal of this analysis is evaluating protein representations over a highly realistic scenario, especially in terms of discovering new drugs and/or targets, using our carefully prepared large-scale datasets, and to compare their overall performance in machine learning-based DTI prediction. Secondly, we aimed to display how model performances can change dramatically when the same samples are distributed to train and test sets differently, to point out the importance of train-test data split. Furthermore, we evaluated the suitability of various performance metrics under different modeling approaches.In this analysis, we constructed protein family-specific bioactivity datasets including enzyme  and non-enzyme groups . For each family, three versions of train-test splits were constructed by considering pairwise similarities of proteins and/or compounds  and 0.26 (le: learned embeddings) in the fully-dissimilar-split, 0.40 (cd) and 0.34 (le) in the dissimilar-compound-split, and 0.84 (cd) and 0.87 (le) in the random-split datasets. For enzyme families, these values are 0.23 (cd) and 0.26 (le) in the fully-dissimilar-split, 0.51 (cd) and 0.52 (le) in the dissimilar-compound-split, and 0.84 (cd) and 0.86 (le) in the random-split datasets. The results show that, in challenging datasets, conventional descriptors perform better on non-enzyme families, while learned embeddings perform better on enzyme families. It suggests that the type of protein representation used can have an impact on the model performance depending on the type/family of protein being studied, possibly due to the intrinsic properties of these protein families. This observation can be useful for developing new strategies to improve model performances. All of the learned representations in our study were obtained from unsupervised deep learning models that are trained on large datasets including all protein families. Limiting the training datasets of these methods to specific families (or fine-tuning the pre-trained models on these families) would increase their representation power towards that family.Results presented in Fig. When taking all these findings into account, we can clearly state that the representation capabilities of different featurization approaches considerably vary among protein families and splitting strategies, even though some common inferences can be made. We believe that, while choosing a featurization approach in DTI prediction, protein family-specific findings should be taken into account, rather than considering the overall  results. Regarding learned representations, re-training (or fine tuning) the models using a distinct dataset with desired characteristics  may be a good choice to better learn the features specifically associated with that group of proteins.To compare models across three dataset splits, we plotted performance scores by pooling 200 models of each split (including the baseline models) without any grouping by families or representation methods. The results are displayed in Fig. In the fully-dissimilar split, neither similar proteins nor similar compounds are shared between train and test folds. As a result, this dataset is suitable to evaluate the performance of DTI prediction models in terms of predicting novel ligands to understudied targets (or completely new target candidates). Whereas in the dissimilar-compound split, similar proteins are presented in between train and test sets. Nevertheless, it is useful for discovering novel ligands against well-studied target proteins, or proteins for which structurally highly similar and well-studied targets exist.Table The average Spearman correlation score of the best performing model on the fully-dissimilar-split dataset is around 0.3, which is quite close to the only-ecfp4 model. This indicates that the success obtained by even the best model has mostly originated from the characteristics of compounds . Thus, these results reveal the requirement for; (i) unbiased model training datasets, and (ii) novel/improved featurization techniques, to construct robust DTI prediction models that can be utilized in the pharmaceutical industry, especially under these challenging scenarios.Model performances are higher on the dissimilar-compound-split dataset compared to the fully-dissimilar-split dataset, due to the inclusion of similar  proteins between training and test. Also, models based on completely random vectors  have lower performances, expectedly. On both of the challenging datasets, the best model is well differentiated from the random vector-based baseline models. Although the overall mean difference between the best model and random200 model is considerably low on the dissimilar-compound-split, the differences are distinct when making protein family-specific comparisons rather than taking the average of all families . On the dissimilar-compound-split dataset, random200 model outperformed the only-ecfp4 model by learning the relationship between the bioactivity data points of the same proteins which are shared between training and test. As experimental bioactivity measurements are mainly obtained from target-based assays, the number of bioactivity data points per protein is considerably high, compared to the number of bioactivity data points per compound . However, high performance scores of the baseline models, including those based on randomly generated vectors , clearly indicate the over-optimistic evaluation, and emphasize the importance of train-test data splitting, once again. These results also demonstrate the importance of baseline model-based investigation in the field of DTI prediction, for a fair and realistic performance evaluation. It is possible to state that, the results reported in previous DTI prediction studies in which (i) the models are only evaluated based on random splitting , and (ii) there is no proper baseline model comparisons, may be invalid.In this experiment, we plotted heatmaps based on pairwise similarities between the protein family-specific PCM model predictions via calculating their intersections, using a categorization composed of six classes . To calculate the similarity between a pair of models, for each bioactivity data point, we count a similar prediction if both models predict pChEMBL values in the same bin (no matter they are correct or not), otherwise we count a non-similar prediction. We then calculate percent similarity values based on all counts. To emphasize prediction similarity values between model pairs, color scales were arranged so that the darkest color corresponds to the maximum value, and the lightest color was set to 85%, 65%, and 20% similarity for the random-split, dissimilar-compound-split, and the fully-dissimilar-split datasets, respectively.In Fig. The results of this analysis can be used to obtain rational combinations of featurization approaches to better represent proteins in DTI prediction models . This may yield a more successful learning of interaction-relevant properties of proteins, and significantly improve the overall model performances.k-nearest neighbors (k-NN) [The concept of AD is used to define the boundaries of a model within which is expected to provide accurate and reliable predictions, and to assess its usability. It has been included as an essential requirement for QSAR models by the Organization of Economic Co-operation and Development (OECD). In the scope of QSAR modelling, AD is defined as the chemical structure space in which the model produces reliable predictions . It is ss (k-NN) , 52 and s (k-NN) .k-NN approach to assess the AD of our models. For this, we first calculated Tanimoto similarities between test and training compounds based on their ecfp4 fingerprints. For each test compound, we calculated the average Tanimoto score of the most similar five training compounds , as described in the study by Subramanian et al. [In this study, we employed the n et al. . Then, we) and similarity values of proteins (p) and compounds (c) based on Fig. e), 66% (p), 77% (c) for random-split, 0.92 (e), 64% (p), 35% (c) for dissimilar-compound-split, and 0.94 (e), 23% (p), 33% (c) for the fully-dissimilar-split, respectively. These values also indicate that the changes in the similarity of compounds have a higher impact on the error, compared to proteins. The results were similar in our other models, as well. It is possible infer from these results that PCM models tend to utilize compound features more than protein features, mostly due to the natural bias in DTI data.The figure displays that most of the data points with high similarities of proteins and compounds have low prediction errors, but there is no direct correlation between similarity and error values as usually observed in QSAR models. At each similarity percentage interval, there are data points with both low and high prediction errors at varying frequencies, even at extremely low similarities. This confirms the extrapolation ability of the PCM modelling approach. However, the number of data points with higher error increases in challenging datasets, which narrows the applicability domain of the models on these datasets. The average prediction error  bioactivity datasets at different scales and their splitting into train-test folds, (ii) preliminary and explanatory analysis of data, (iii) different modeling and algorithmic approaches, (iv) the representation capability of various protein featurization techniques, and (v) robust and fair performance evaluation strategies, for machine learning-based DTI prediction modelling. For this, we built target feature-based and PCM-based models, and trained/tested them on carefully constructed datasets with varying sizes and difficulty levels, using numerous protein representations, and evaluated them from different perspectives. Datasets, results and the source code of the study is fully shared in our “ProtBENCH” platform at (i)We proposed challenging benchmark datasets with high coverage on both compound and protein spaces that can be used as reliable, reference/gold-standard datasets for DTI modelling tasks. These datasets are protein family-specific, and each has three versions in terms of train/test splits for different prediction tasks . Thus, they yield fair evaluation of models at multiple difficulty levels and facilitate the prevention of over-optimistic performance results. We evaluated these datasets in the framework of PCM modeling, which is a highly promising data-driven approach for high performance ML-based drug discovery. These datasets can be used in future studies to evaluate newly proposed modeling and/or algorithmic techniques for DTI prediction.(ii)We employed a network-based strategy for splitting data into train-test folds, by considering both protein–protein and compound-compound pairwise similarities, which is proposed here for the first time, according to our knowledge. This strategy ensures that train and test folds are totally dissimilar from each other with a minimum loss of data points. One of the current limitations in drug development is the problems related to discovering novel molecules that are structurally different from existing drugs and drug candidates. The network-based splitting strategy we applied here forces prediction models to face this limitation by supplying more realistic, hard-to-predict test samples. Hence, it can aid researchers in designing more powerful and robust DTI prediction models that have a real translational value.(iii)Protein representation learning has a wide range of applications with promising results in different sub-fields of protein science, despite being a relatively new approach. However, the studies regarding their usage in DTI prediction modelling are limited, and there is no comprehensive benchmark study to evaluate their performance against well-known and widely used featurization approaches. Due to this reason, we extended the scope of our study by involving state-of-the-art learned representations and discussed their potential in DTI prediction.Below, we summarized the major contributions of our study to the literature:One of the critical observations of this study is the dramatic change in performance scores when the samples are distributed to train and test sets differently, , which highlights the importance of data splitting to conduct realistic evaluations for drug and/or target discovery. This study also emphasizes the importance of exploratory analysis of datasets and the usage of multiple scoring metrics as well as the inclusion of baseline models for a proper discussion of model successes.Regarding the performance-based comparison of different protein featurization approaches, it is not possible to put forward an outstanding representation method, as their success largely depends on the dataset and the applied splitting strategy. Although both conventional descriptor sets and learned embeddings have their own strengths and weaknesses depending on the case, competitive results of learned embeddings display their potential wide-spread utilization in drug discovery and development in the near future. On the other hand, considerably low performance results on challenging datasets  in the overall evaluation revealed the requirement of unbiased bioactivity datasets and further improved protein representation techniques to capture hidden and complex features shared between highly distant homologs.As future work, we plan to develop a new computational DTI modeling approach that utilizes numerous types of biological and biomedical entities on top of compounds and target proteins. Drug discovery and development is composed of a series of complex problems, and there are multiple factors affecting the success of a drug candidate. This is mainly due to the extremely dynamic and complicated structure of biological systems. As a result, it is not possible to computationally handle drug discovery solely by simple virtual screening. Considering this fact, taking a systems-based approach with the integration and utilization of direct and indirect relationships in molecular and cellular processes including protein–protein interactions, drug/compound–target protein interactions, and signaling/metabolic pathways, together with high level concepts such as protein-disease relationships, drug-disease indications, pathway-disease modulations, and phenotypic implications could increase the success rate in drug discovery. Thus, we aim to construct a new type of systems-level DTI representation and subsequent prediction framework, using CROssBAR  which isWe hope that the results of this study, together with the data-driven approaches proposed, and the benchmark datasets prepared and shared, will aid the ongoing work in computational drug discovery and repurposing.In this section, we first explain the construction of benchmark datasets, with emphasis on the train/test data splitting strategies. Next, we explain featurization techniques used for representing of proteins and compounds. Then, we summarize modelling approaches and algorithms employed for DTI prediction, along with additional explorative analyses such as the t-SNE projections. Finally, we mention performance evaluation metrics and the tools/libraries we employed.In machine learning applications, two significant factors that affect the generalization capability of models are the dataset content/size and the approach used in splitting data points to train/validation/test folds. We constructed and used three groups of datasets at different scales , each of which have distinctive characteristics.Here, the aim is to construct datasets of target proteins to be used in DTI prediction models, in which the only input is target feature vectors, and the task is to classify them to their correct ligands. Each dataset is composed of targets of a specific drug/compound as reported in the ChEMBL (v24) database  consideronly contains active or inactive data points based on the threshold pKd = 6.2, which is the median bioactivity value of the dataset,has an active-to-inactive ratio > 4 or < 1/4 considering its bioactivity data points,has a bioactivity distribution with standard deviation < 0.3, which means bioactivity values vary within a narrow range.We employed the previously proposed Davis kinase dataset  for perfA successful machine learning model is expected to learn general principles from data rather than memorizing it. The instances fulfilling the conditions above may not contribute to the learning process, as they can be easily predictable regardless of the algorithm or feature set, since they have very similar outcomes. We removed these instances from the dataset; otherwise, the model would perform well just by memorizing the outcome of these cases. After these filtering operations, the finalized set, which we call the modified Davis (mDavis) dataset, contains 6,706 train and 1,542 test data points. This dataset is used in the medium-scale PCM-based analysis.With the aim of constructing large-scale gold standard datasets, we applied rigorous filtering operations on the recorded bioactivities of target proteins from different protein families including membrane receptors, ion channels, transporters, transcription factors, epigenetic regulators, and enzymes with five subgroups . Protein family information is taken from the ChEMBL  target pFor enzymes, subclasses belonging to the same main class were merged based on their EC number annotations. The merged enzyme classes and their corresponding EC numbers are given in Additional file For each protein family-based dataset, three types of train-test folds were extracted based on different dataset splitting strategies using molecular similarities in-between compounds and proteins. For this, we binarized pairwise similarity measurements as “similar” or “non-similar”. UniRef50 clusters  were useThis dataset is constructed by applying a complete random splitting strategy, so that similar compounds and proteins are presented in both train and test sets. Random splitting is one of the most widely used dataset split strategies in machine learning applications; however, it eases the prediction task due to the sharing of highly similar instances between train and test sets. Thus, models usually display overoptimistic performance results. In our random-split protein family-specific datasets, at least 95% of proteins and 60% of compounds in test sets are found to be similar to the ones in their respective train sets.This dataset is constructed by applying a strategy that only considers compound similarities while distributing bioactivity data points into train-test splits. Compounds in train and test splits are dissimilar to each other (Tanimoto score < 0.5). Therefore, similar compounds are not allowed to take part in both train and test splits. This strategy makes the prediction task more difficult and realistic compared to random splitting and partly prevents the model from memorizing bioactivities over identical or highly similar compound fingerprints shared between train and test folds.Protein–protein and compound-compound pairwise similarity matrices were constructed independently for each protein family, based on protein family membership information and interacting compounds for those proteins (obtained from ChEMBL bioactivity data points). Similarity values were binarized according to the procedure explained above .A heterogeneous network was constructed for each protein family by merging similarity matrices and bioactivity data using the NetworkX Python library , where nIn order to overcome this issue, we preferred to discard some of the nodes  and edges  from the dataset to subdivide the giant connected components into smaller pieces. Instead of removing nodes and edges randomly, which may cause the loss of a high number of data points, we employed the Louvain heuristic algorithm  to detecThe aim here is to create train test folds in a way that neither compounds nor proteins are similar to each other between train and test. This dataset is constructed using a network-based splitting strategy to separate bioactivity data points  into disconnected components. Later, each component is either used in training or test splits. Actually, this dataset is extremely challenging for any DTI prediction method. However, this approach is crucial to evaluate a DTI prediction model’s ability to accurately predict new targets and/or new ligands that are truly novel , as this is one of the most crucial expectations from the PCM modeling approach. The steps of the network-based splitting process are provided below:Discarded data points of the fully-dissimilar-split dataset were also excluded from training-test folds of random-split and dissimilar-compound-split datasets for keeping instances of three sets exactly the same, to yield fully comparable results. The sizes of these datasets (after discarding data points) range from 18,164 to 206,175 depending on the protein family. Detailed split-based statistics are provided in Additional file We converted proteins and compounds into fixed-length numerical feature vectors to be used in our DTI prediction models as input samples. The following sub-sections describe different featurization approaches used in this study.On the basis of sequence-based modeling approaches utilized, we divided this subsection into two categories as conventional protein descriptors and learned protein embeddings. These methods are explained below in terms of their molecular and technical aspects. Names, descriptions, and feature vector dimensions of these descriptors/representations are given in Table apaac (amphiphilic pseudo amino acid composition) represents the amino acid composition of protein sequences without losing the residue order effect by using sequence-order factors. These factors are computed from correlation functions of hydrophobicity and hydrophilicity indices of amino acids. Therefore, apaac keeps the distribution of amphiphilic amino acids along the protein chain. It was proposed by Chou in 2005 and used for the prediction of enzyme subfamily classes [ classes .ctdd (distribution) provides distribution patterns of amino acids in terms of the class they belong to considering a particular property. It utilizes 7 types of physicochemical properties including hydrophobicity with 7 different versions, normalized Van der Waals Volume, polarity, polarizability, charge, secondary structures, and solvent accessibility. Each property is divided into 3 classes and 20 amino acids are distributed into these classes based on their values for corresponding property . The distribution patterns are determined according to five different positions (residues) for the corresponding class, which are the first residue, and the residues exactly at the 25%, 50%, 75%, and 100% of the sequence. These positions are divided by the length of the whole protein sequence for the calculation of fractions of each class. This descriptor set was first proposed by Dubchak for protein fold recognition task [ion task .ctriad (conjoint triad) is based on the frequency of triple amino acid combinations in a protein sequence, where amino acids are first converted into a 7-letter reduced alphabet. These seven groups include {A,G,V}, {I,L,F,P}, {Y,M,T,S}, {H,N,Q,W}, {R,K}, {D,E}, and {C}. Amino acid groups are determined according to dipoles and volumes of the amino acid side chains. Ctriad was first used for the prediction of protein–protein interactions by Shen et al. [n et al. .dde (dipeptide deviation from expected mean) is a type of sequence composition descriptor set that relies on the deviation of dipeptide compositions from the expected means. Three parameters, i.e., dipeptide composition (Dc), theoretical mean (Tm), and theoretical variance (Tv), are computed for the construction of the dde descriptor set. Saravanan and Gautham proposed it in 2015 for the use of B-cell epitope prediction [geary utilizes the distribution of structural and physicochemical properties of amino acids along the sequence. It was first developed by Geary in 1954 [ in 1954  as a mea in 1954 . Also, O in 1954 .k-sep_pssm (k-separated-bigrams-pssm) is a column transformation-based descriptor set that computes the bigram transition probabilities between residues in terms of their positional distances from each other, which corresponds to the “k” value. The transition probabilities are calculated from transformations on position-specific scoring matrix (pssm) profiles of proteins. Pssm profiles represent evolutionary conservation of amino acids in a protein sequence, which are derived from multiple sequence alignments of a homolog set of protein sequences. This descriptor set was first proposed in the study of Saini et al. for improving protein fold recognition [ognition . Wang etognition .pfam represents domain profiles of proteins, according to protein domain annotations in the Pfam database [database , in the database  and Liu database  with theqso (quasi-sequence order) reflects the indirect effect of the protein sequence order by calculating coupling factors in terms of distances between contiguous residues in the sequence. The distances are determined using different amino acid distance matrices such as the Schneider–Wrede distance matrix [e matrix , which ie matrix .spmap (subsequence profile map) is a feature space mapping method that represents sequence composition by calculating the distribution of fixed-length protein subsequence (with a length of 5 residues in the default version) clusters in a protein sequence. Subsequence clusters are generated using BLOSUM62 matrix-based similarities of all possible subsequences in the given protein set, extracted by the sliding windows approach. It was proposed by Sarac et al. for functional classification of proteins [proteins . Later, proteins  and EC nproteins  predictitaap  represents the total sum of corresponding residue values in a protein sequence for the selected properties from the AAIndex database [database . It was database . The proThis category comprises methods that employ model-driven approaches. This is achieved by transforming various molecular properties of proteins, such as sequence composition, evolutionary relationships, functional characteristics, or physicochemical properties of amino acids, into fixed-length numerical feature vectors with the implementation of predefined rules or statistical calculations. Hence, they convert protein sequences into a quantitative and machine-processible format that stores the relevant molecular information. Ten conventional protein descriptor sets used in all 3 of the benchmark analyses of this study are briefly explained below.iFeature stand-alone tool  was emplThis category comprises protein representations that utilize solely data-driven approaches for the extraction of molecular information from protein sequences. Learned representations are constructed via the process of artificial learning, in which a model is trained on specific unsupervised/self-supervised tasks such as the prediction of the next amino acid in the sequence. For generating such protein representation models, deep neural network-based architectures and design choices that are frequently used in NLP field are preferred. During the training process, the model takes protein sequences as input, projects them into a high-dimensional vector space, and generates output in the form of fixed-length numerical feature vectors called “embeddings”. These numerical feature vectors can later be used for representing proteins in other predictive tasks (mostly supervised) such as DTI prediction.unirep is one of the best-known learned protein representations, which was developed in 2019 by Alley et al. using a variation of recurrent neural networks (RNN) called the multiplicative long-/short-term-memory (mLSTM) architecture [https://github.com/churchlab/UniRep.itecture . Alley etransformer is a deep architecture that utilizes the attention mechanism in a way to allow the extraction of context without depending on the sequential order information in the input samples [https://github.com/songlab-cal/tape. samples , and it  samples . This mo samples , via masprotvec was developed by Asgari and Mofrad [http://dx.doi.org/10.7910/DVN/JMFHTN.d Mofrad  as one oseqvec utilizes bi-directional language model architecture of the “Embeddings from Language Models (ELMo)” method for extracting features relevant to per-residue and per-protein prediction tasks. Heinzinger et al. developed the seqvec model by training on approximately 33 million UniRef50 sequences with the goal of predicting the next amino acid in the sequence [https://github.com/rostlab/SeqVec.sequence . The autFour protein representation learning methods  used in this study are briefly explained below.We constructed dummy feature vectors (to be used in baseline prediction models) for performance comparison against real representations, with the aim of observing to what degree proteins descriptors are utilized by DTI prediction models. The one for proteins, namely random200, is a descriptor that constructs a feature vector (with the size of 200 × 1) for each protein sequence containing randomly generated continuous values ranging from 0 to 1 in each dimension.We employed the (circular) fingerprinting approach, and used Extended-Connectivity Fingerprints (ECFPs) as feature vectors (representations) of compounds. ECFPs are circular topological fingerprints that are widely used for molecular characterization, similarity searching, and structure–activity relationship modeling. ECFPs represent the presence of particular substructures by considering circular atom neighborhoods within a diameter range . We consIn order to evaluate different protein featurization methods on DTI prediction, we utilized 2 different modelling approaches: (i) target feature-based modelling, and (ii) PCM modelling. Below, we summarized each approach together with the implementation details.In this modelling approach , we trained an independent DTI prediction model for each selected drug/compound cluster on small-scale  values of the input samples  in terms of pKd/pChEMBL values. We constructed PCM models for 10 conventional protein descriptor sets, including apaac, ctdd, ctriad, dde, geary, k-sep_pssm, pfam, qso, spmap and taap, and 6 learned representations including protvec, seqvec, transformer-avg, transformer-pool, unirep1900, and unirep5700. Since PCM models are based on compound-target pairs, protein representations were concatenated with 1024 bits ECFP4 representations of compounds to construct the finalized input feature vectors.We generated two baseline models  by concatenating random200 protein feature vectors (size: 200 × 1) with (i) real ECFP4 fingerprints, and (ii) random compound fingerprints, which are named “random200” and “random200-random-ecfp4” models, respectively. Furthermore, we constructed two additional baseline models to be used in the large-scale analysis, in which the protein features are not utilized at all. In the first one, we used the real ECFP4 fingerprint of the compound in the corresponding compound-target pair to represent the pair , and in the second one, we used random compound fingerprints of size 1024 × 1 to represent input pairs . Thus, we trained and tested 18 PCM models for the medium-scale analysis using the mDavis kinase dataset . For the large-scale analysis, we trained and tested models for 20 featurization types  on 10 protein family-specific datasets each having 3 versions of train-test split folds. Therefore, we constructed 600 PCM models .t-SNE is a non-linear dimensionality reduction technique that is frequently employed for the visualization of high dimensional datasets . For expThe performance of target feature-based classification models  was evaluated with accuracy, precision, recall, F1-score, and MCC metrics via nested cross-validation. F1-score is the harmonic mean of precision and recall; thus, it takes both false positive and false negative predictions into account. It is a widely used metric that balances precision and recall. However, it doesn’t consider true negative predictions and can be biased in imbalanced datasets. MCC incorporates all true and false predictions into the equation, and it is preferred over both accuracy and F1-score due to its robustness and reliability, especially in the cases of dataset imbalance .s) metrics over the hold-out test sets. RMSE computes the deviation of predictions from the actual values, and lower RMSE scores indicate better model performance. Spearman correlation evaluates the relationship between the ranks of the predicted and actual values. One of the problems related to regression-based prediction models is that the distribution of predicted values can have a shifted average . Value-based performance metrics suffer from this problem and report underestimated scores. In order to handle this problem in the large-scale analysis (where the problem is evident), we calculated an additional version of RMSE via median correction, so that the median value of predictions becomes equal to the median of the true value distribution .The performance of PCM-based regression models  was evaluated using RMSE and Spearman rank correlation , samples were classified as active (1) or inactive (0) based on an activity cut-off value of pKd = 7  using the RF classification algorithm. For the large-scale analysis over protein family-specific datasets, regression-based prediction results were converted into binary class and multiclass formats, as it was not possible to retrain 600 models due to high computational requirements. For the binary class, median pChEMBL values of the data points in the training datasets were used as threshold values to separate actives and inactives from each other . We also calculated corrected version of MCC using the procedure explained above for “median corrected RMSE” score, and similarly called this metric the “median corrected MCC”. For the calculation of multi-class scores, samples were placed into six different classes based on their true pChEMBL values  and calculated average MCC scores over all 6 classes. The reason behind using such a variety of performance metrics was to evaluate models from as many different aspects as possible.Di = R programming language, scikit-learn library  for the Additional file 1: Table S1.. Model performance scores (in terms of MCC) in the small-scale analysis (on the compound-centric datasets) for; (a) RF, and (b) SVM models. The 3 best performances for each dataset are shown in bold font. For the results based on other performance metrics please see the supplementary spreadsheets attached as external files named: “small-scale_rf_overall_test_results.xlsx” and “small-scale_svm_overall_test_results.xlsx”. Table S2. Model performance scores in the medium-scale analysis (on the mDavis dataset). The best performance for each metric is shown in bold font. Table S3. Model performance scores (in terms of the median corrected MCC) in the large-scale analysis on the protein family specific datasets of; (a) the random-split, (b) dissimilar-compound-split, and (c) the fully-dissimilar split.The 3 best performances for each protein family are shown in bold font . For the results based on other performance metrics please see the supplementary spreadsheet attached as an external file named: “large-scale_overall_test_results.xlsx”. Table S4: Prediction error percentages of transformer-avg models with different thresholds on random, dissimilar-compound, and fully-dissimilar splits of transferases family dataset. Table S5. Statistics of the protein family specific datasets used in the large-scale analysis. Table S6. Split-based statistics of the protein family specific datasets for; (a) the random-split, (b) dissimilar-compound-split, and (c) the fully-dissimilar-split sets (ratio represents the ratio of train to test). Table S7. Statistics of the compound-centric datasets used in the small-scale analysis. Table S8. Enzyme sub-classes of the protein family specific datasets used in the large-scale analysis. Figure S1. t-SNE based visualization of conventional and learned protein representations on; (a) enzymes including hydrolases, oxidoreductases, proteases, transferases, and other-enzymes, and (b) other protein families (non-enzymes) including epigenetic regulators, ion channels, membrane receptors, transcription factors and transporters. Figure S2. Bioactivity distributions of protein family-specific datasets in terms of; (a) random split, (b) dissimilar-compound split, and (c) fully-dissimilar split sets, together with the median values shown as vertical dashed lines. Figure S3: KS distance (between train and test samples) score distributions of (a) apaac, and (b) transformer-avg representations among random, dissimilar-compound, and fully-dissimilar splits in the transferases family proteins. Figure S4. Clustered heatmaps of different protein representation approaches for protein families on (a) the random-split, (b) dissimilar-compound-split, and (c) fully-dissimilar-split datasets.


To determine the efficacy and safety of blue-light therapy in seasonal andnon-seasonal major depressive disorder (MDD), by comparison to active andinactive control conditions.We searched Web of Science, EMBASE, Medline, PsycInfo, and Clinicaltrials.govthrough January 17, 2022, for randomized controlled trials (RCTs) usingsearch terms for blue/blue-enhanced, light therapy, and depression/seasonalaffective disorder. Two independent reviewers extracted data. The primaryoutcome was the difference in endpoint scores on the Structured InterviewGuide for the Hamilton Depression Rating Scale - Seasonal Affective Disorder(SIGH-SAD) or the Structured Interview Guide for the Hamilton DepressionRating Scale with Atypical Depression Supplement (SIGH-ADS) between bluelight and comparison conditions. Secondary outcomes were response  and remissionrates (endpoint score in the remission range).n = 347participants) assessing blue-light therapy. Seven studies had participantswith seasonal MDD and two studies included participants with non-seasonalMDD. Four studies compared blue light to an inactive light condition(efficacy studies), and five studies compared it to an active condition(comparison studies). For the primary outcome, a meta-analysis withrandom-effects models found no evidence for the efficacy of blue-lightconditions compared to inactive conditions ;however, blue-light also showed no differences compared to active conditions. There wereno significant differences in response and remission rates betweenblue-light conditions and inactive or active light conditions. Blue-lighttherapy was overall well-tolerated.Of 582 articles retrieved, we included nine RCTs (The efficacy of blue-light therapy in the treatment of seasonal andnon-seasonal MDD remains unproven. Future trials should be of longerduration, include larger sample sizes, and attempt to better standardize theparameters of light therapy. Lux is a measure of illumination that varies with the distance to the lightsource. For comparison, indoor social lighting is rated as less than 100 lux, brightoffice lighting at 500 lux, outdoors on a cloudy day at 5,000 lux, and outdoors on asunny day at 50,000 lux or higher. Light therapy is generally well-tolerated with few or mild side effects.Light therapy consists of daily exposure to bright fluorescent light that istypically delivered at home via a light device such as a light box. Light therapyhas been extensively studied in seasonal affective disorder (SAD), but increasingevidence suggests that it is also effective in non-seasonal major depressivedisorder (MDD) and bipolar depression.10 Light is the strongest synchronizer of circadian rhythms, withthe circadian effects of light acting through the eyes via the retinohypothalamictract, a direct neural pathway from the retina to the suprachiasmatic nucleus, whichis recognized as the central biological clock in the brain. More recently, it was shown that melanopsin, a photopigment located inretinal ganglion cells, modulates the circadian effects of light. Melanopsin is particularly sensitive to wavelengths of light in the bluecolor range , and low-intensity blue light can shift circadian rhythms as effectively ashigher-intensity white light. In addition, melanopsin plays an important role in suppressing melatoninproduction, as well as improving alertness and neurobehavioral performance, whichmay mediate some of the antidepressant effects of light.15 There is also evidence thatblue light may promote affective arousal and modulate emotional brain responses,notably in areas involved in depression such as the amygdala, hippocampus, and hypothalamus. These recent findings suggest specific wavelength hypotheses for lighttherapy for depression, including (1) blue light at low intensity may beefficacious, which could have advantages such as fewer side effects and shortertreatment time than higher intensity white light, and (2) enriching high-intensitywhite light with blue wavelengths (we will refer to this as blue-enhanced whitelight throughout the text) may be more effective than standard white light.Although the precise mechanism of action of light therapy remains unclear, it ishypothesized that alterations in circadian rhythm, suppression of melatoninsecretion, and modulation of serotonin may be important contributoryfactors.5 Our objective is tosystematically review the literature on randomized controlled trials (RCTs) usingblue light for depression and to examine the efficacy and safety of blue light fordepressive disorders. We will compare blue light to inactive and active controlconditions. We will also conduct sensitivity analyses for studies usinglow-intensity blue light  and for studies that includedonly participants with SAD.To our knowledge, there are no quantitative syntheses looking specifically at theeffects of blue-light therapy on patients with seasonal and non-seasonal MDD.Previous meta-analyses of light therapy focused on non-seasonal MDD and did notdistinguish between specific wavelengths of light.www.crd.york.ac.uk/prospero/, CRD#42021239374) and conductedfollowing Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines, but a protocol was not published. The systematic searchincluded studies up to January 17, 2022, and was conducted using the followingdatabases: Web of Science, EMBASE (OVID), Medline (OVID), PsycInfo, andClinicaltrials.gov. The search strategy contained the following terms: (blue ORblue-enhanced OR blue-enriched OR narrow-band OR wavelengths OR spectrum) AND(phototherap* OR “light therapy” OR “light treatment”) AND . Medical subject heading (MeSH) termswere included when available. To look for additional studies that may have notbeen captured by the original database search, we performed backwards referencechaining by searching through bibliographies of relevant articles.The systematic review was registered with PROSPERO . Studies were excluded if: (1) the participants had other comorbidconditions as a primary diagnosis; and (2) the active intervention included acombination of light therapy with another treatment and the comparison condition did not include the other treatment. Abstracts,case reports, case series, and review articles were also excluded.Studies were included if: (1) they were published RCTs; (2) the activeintervention was low-intensity blue light or blue-enhanced white light; (3)participants met diagnostic criteria  for a majordepressive episode, seasonal or non-seasonal; and (4) a clinician-rated measureof depressive symptomatology was used  screened titles and abstracts of articlesretrieved by the search for inclusion. Potentially eligible articles werefurther reviewed by reading the full text. Any initial disagreements between thereviewers were resolved by joint review, discussion, and consensus or throughconsultation with an independent third reviewer (RWL).Two independent reviewers  extracted the data using a data extractionform designed for the study. Any disagreement was resolved by consensus orthrough consultation with a third reviewer (RWL). Study authors were contactedif eligible data were not reported in the paper. The following data wereextracted: participant demographics ,study characteristics , details of the active intervention,and comparison conditions, outcomes measures and scores at baseline andendpoint, response/remission rates, and adverse events. to assess bias in the following categories: randomization process,deviations from interventions, missing outcome data, measurement of the outcome,selection of reported outcomes, and overall bias.We used version 2 of the Cochrane risk-of-bias tool for RCTs or the Structured Interview Guide for the Hamilton Depression RatingScale with Atypical Depression Supplement (SIGH-ADS) between the active and comparison conditions. The SIGH-SAD is a 29-itemclinical interview comprising the 21-item HDRS with 8 additional items foratypical depressive symptoms. The SIGH-ADS is a 25-item clinical interview thatincludes the 17-item HDRS with the same 8 items for atypical symptoms. Secondaryoutcomes included: (1) clinical response , and (2)clinical remission rates (endpoint score in the remission range). We alsoexamined tolerability using acceptability  anddropouts due to adverse events.The primary outcome was the difference in endpoint scores on the StructuredInterview Guide for the Hamilton Depression Rating Scale - Seasonal AffectiveDisorder (SIGH-SAD) to guard against the inflation of effect size for studies with more thanone intervention arm, either the results of all active treatment arms werepooled as one intervention or the control group size was divided by the numberof active intervention arms. For crossover studies, we included only data forthe first arm of the crossover.All outcomes were analyzed with the intent-to-treat samples (ITT) if available.Since all the studies (except Danilenko 2019) used either the SIGH-SAD orSIGH-ADS as the main clinician-rated depression rating scale, the primaryoutcome was analyzed using mean differences (MD) as a measure of effect size. Ifendpoint scores were not available, change scores (endpoint minus baselinescores) were used. The secondary outcomes were analyzed using odds ratios (ORs).Outcome data were extracted at the end of treatment for each study unlessotherwise specified. As per Cochrane recommendations,For our primary analyses, we compared blue-light conditions, which include bothlow-intensity blue and blue-enhanced white light, to inactive conditions  and separately to active conditions . In addition, we conducted sensitivity analyses of(1) studies using low-intensity blue light to test the hypotheses thatlow-intensity blue light has efficacy compared to inactive conditions or may beequal or superior to white light, and (2) studies including only SADparticipants.I2; an I2 of 50% to70% suggests moderate heterogeneity and 75% to 100%, high heterogeneity. Publication bias was determined with: (1) funnel plots of outcomesplotted against their standard error, (2) Rosenthal's fail-safeN (the number of unidentified negative studies that wouldneed to exist to change the result), and (3) Egger's regression intercept . The Trim and Fill procedure was used to impute missing studies ifpublication bias was suggested. The quantitative meta-analysis was done using Comprehensive Meta-AnalysisVersion 2.0 software .Since we anticipated heterogeneity in study methodologies, such as variations inthe type of depression , study duration, andtype of control condition, we used a random-effects model. Statisticalheterogeneity was assessed using Q chi-square statistics and the design was not randomized, or the active intervention was not low-intensity blue or blue-enhancedwhite light, resulting in a total of nine studies for the meta-analysis.3038n = 347participants). Seven studies included participants with a primary diagnosis ofSAD , and two studies recruited patients with non-seasonal MDD; all the participants were diagnosedaccording to the DSM-IV, DSM-IV-TR, or DSM-5 criteria. One study (Anderson 2009)included three patients with bipolar II disorder, but the authors did notanalyze the data separately for the bipolar subgroup. All the studies involvedmiddle-aged participants, except for Lieverse et al., which included olderpatients. Most studies had small sample sizes, ranging from 18 to 84participants.Eight of the included RCTs involved daily light exposure without sleepdeprivation for at least 5 days; one (Danilenko 2019) used a 6-day protocolconsisting of partial sleep deprivation alternating with morning lighttreatment, but both treatment groups received sleep deprivation. The lightparameters for both the active and comparison conditions varied across studies.For the active condition, six studies  used low-intensity blue light,while two studies  used blue-enhanced white light.The blue light intensities ranged from 98 to 10,000 lux. The blue lightwavelength remained consistent across studies, ranging from 450 to 470 nm. Forthe comparison conditions, four studies  used standard white light , three  used dim redlight (intensity ranging from 23 to 201 lux), one (Anderson 2016) used anorange-appearing medium-wavelength light (average intensity of 120 lux) and one(Danilenko 2019) used orange-appearing glasses blocking wavelengths below540 nm. For all the studies except Danilenko et al. and Gordjin et al., thedaily light exposure was either 30, 45, or 60 min per day. The Gordjin et al.study had three treatment conditions: (1) 30 min standard light therapy, (2)30 min blue-enhanced white light therapy, and (3) 20 min blue-enhanced whitelight therapy. The study treatment durations varied from 5 to 42 days.The primary outcome was the difference in endpoint scores on the SIGH-SAD orSIGH-ADS between blue-light therapy and comparison conditions. Six studiesreported endpoint scores; Strong et al. reported change scores and Glickman etal. reported a between-group mean difference. We excluded Danilenko et al. fromthe primary outcome analysis because it was the only study that did not useeither the SIGH-SAD or SIGH-ADS. Since the control conditions included inactiveand active conditions, we reported them separately. Studies comparing blue lightto an inactive condition were considered efficacy studies, while studiescomparing blue light to an active condition were considered comparisonstudies.P = 0.20; 4 studies, total n = 169 participants) . The funnel plot of standard errors by effectsize estimates was broadly symmetrical, the fail-safe N was 0since the outcome was nonsignificant, and the Egger's intercept was 4.38(two-tailed P = 0.17), suggesting a low probability ofpublication bias.cipants) . There wP = 0.07; 3 studies, n = 149). There was no heterogeneity between thestudies, with a Q statistic of 1.38 andI2 of 0% .The unadjusted response rates for blue light and inactive conditions were 56%and 41%, respectively. For remission rates, comparison to inactive conditionsrevealed an OR of 1.21  . There was no heterogeneity between thestudies, with a Q statistic of 0.57 andI2 of 0% .The unadjusted remission rates were 55% for blue light and 50% for inactiveconditions.For response rates, comparison to inactive conditions produced an OR of 1.93 infavor of blue-light therapy that was not statistically significant , anda low probability of publication bias .For comparison studies, there was no difference between blue light and activeconditions in the endpoint SIGH-SAD or SIGH-ADS scores . TherewSupplementary Figure 1C and D). For response rates, the OR was1.17  with moderate heterogeneity between the studies. The unadjusted response rates for blue light andactive conditions were 69% and 66%, respectively. For remission rates, the ORwas 0.72  with no heterogeneity between the studies. The unadjusted remission rates for bluelight and active conditions were 54% and 60%, respectively.There were also no significant differences between blue light and activeconditions in response and remission rates . There was moderate heterogeneity between thestudies . For comparison studies in SAD, low-intensityblue light performed similarly to active conditions . There wasno heterogeneity between the studies .In total, six studies used low-intensity blue light ranging from 98 to 750 lux.These studies included only participants with SAD. For efficacy studies in SAD,there was no difference between low-intensity blue light and inactive conditions, three studies did notspecify in which group they occurred, so a meta-analysis was not possible;instead, we provide a qualitative review. Common reasons for all-causediscontinuation included a poor response to treatment , scheduling conflicts ,inability to follow treatment schedule (Anderson 2009), medication switch(Lieverse 2011), worsening of depression (Lieverse 2011) and medical illness.Similarly, data on dropout rates due to adverse events were reportedinconsistently across studies and some studies did not specify in whichtreatment group they occurred, hence a meta-analysis was not possible andinstead we provide a qualitative review. Overall, both blue-light therapy andthe comparison conditions were well tolerated. The most common side effectassociated with blue-light therapy was headache. In total, three participantsdropped out prematurely due to side effects related to the study device. In theAnderson et al. study, one patient in the blue light group dropped out afterexperiencing a migraine and a “hot spot on the eye,” while one patient in thewhite light group dropped out due a combination of headache, migraine, eyestrain and the report of a white flashing light. The Strong et al. study did notspecify why the patient dropped out. No switches to hypomania or mania weredescribed, but four studies did not report on switch rates.efficacy of blue-light therapy since our primary andsensitivity analyses for studies with inactive conditions were both negative. On theother hand, the comparison studies revealed that blue light and active conditionsperformed similarly in both primary and sensitivity analyses. This may suggest thatlow-intensity blue light could be as effective as standard lighttherapy conditions; however, it must be pointed out that the comparison betweenblue-light therapy and active conditions was not powered for non-inferiority.Regarding safety and tolerability, although we were unable to provide a quantitativesynthesis, the qualitative review suggested that blue-light therapy was generallywell-tolerated by patients, with only mild side effects and no serious adverseevents or hypomanic/manic switches reported.To our knowledge, this is the first systematic review and meta-analysis of RCTs ofblue-light therapy for seasonal and non-seasonal MDD. Overall, we found mixedresults for efficacy and comparison studies. The meta-analysis found no significantdifference between blue-light therapy and comparison conditions (either active orinactive conditions) in reducing the primary outcome of depressive symptom scores asmeasured by the SIGH-SAD or SIGH-ADS. Hence, there is no current evidence supportingthe These results should be interpreted with caution given the limitations of ourmeta-analyses. First, the included studies had variable heterogeneity and quality.For example, the light parameters for both active and inactive conditions, durationof daily light exposure, and duration of study treatment varied considerably acrossall the studies. Both seasonal and non-seasonal depression were included, althoughthe sensitivity analyses with only SAD studies were no different than the primaryanalyses. One study (Anderson 2009) also included three patients with bipolardisorder. Regarding study quality, only one of the nine studies had an overall lowrisk of bias. Second, the included studies had small sample sizes that were likelyunderpowered, limiting their ability to detect a treatment effect. Third, since allthe studies were of short treatment duration , the longer-term effects of blue-light therapy remainunstudied. Third, there was considerable heterogeneity in the type of light device used,ranging from larger light boxes to small, portable LED devices. Compared to standardfluorescent light boxes, these smaller devices may have greater variability in thepositioning of the patient during use, leading to suboptimal light exposure to theeyes. Finally, although low-intensity blue light can suppress melatonin and shifthuman circadian rhythms, the circadian effects of light may not mediate its antidepressant effects. InSAD, the evidence to support a circadian hypothesis for the antidepressant mechanismof light therapy remains sparse, and alternate mechanisms have been hypothesized, including non-circadianeffects of bright light on neurotransmitters such as serotonin anddopamine.41 The lack of difference between blue light and active conditionsmay be the result of placebo effects since the comparison studies did not include aplacebo control condition to validate the efficacy of the active condition, or dueto small sample sizes.Given these limitations, there are several possible explanations for the lack ofefficacy of blue-light therapy reported in our meta-analysis. First, the smallnumber of included studies may have led to the non-rejection of a false nullhypothesis (type II error). Second, the benefit of blue-light therapy may takelonger to demonstrate than the short treatment duration of the included studies. Forexample, clinical trials have suggested that the separation of active lightconditions from sham conditions may take four weeks or longer.In summary, there is no current evidence for the efficacy of blue-light therapy inthe treatment of seasonal and non-seasonal MDD, according to our meta-analysis.However, given our finding that blue light performed similarly to active conditions,better quality studies are needed to demonstrate the efficacy of blue light indepression. Future trials of blue-light therapy should be of longer duration,include larger sample sizes, and attempt to better standardize the parameters oflight therapy. Further investigation is also necessary to determine the optimaldosing parameters  of lighttherapy for both seasonal and non-seasonal depression.Click here for additional data file.Supplemental material, sj-docx-1-cpa-10.1177_07067437221097903 for Blue-LightTherapy for Seasonal and Non-Seasonal Depression: A Systematic Review andMeta-Analysis of Randomized Controlled Trials by André Do, Victor W. Li,Samantha Huang and Erin E. Michalak, Edwin M. Tam, Trisha Chakrabarty, LakshmiN. Yatham, Raymond W. Lam in The Canadian Journal of PsychiatryClick here for additional data file.Supplemental material, sj-pptx-2-cpa-10.1177_07067437221097903 for Blue-LightTherapy for Seasonal and Non-Seasonal Depression: A Systematic Review andMeta-Analysis of Randomized Controlled Trials by André Do, Victor W. Li,Samantha Huang and Erin E. Michalak, Edwin M. Tam, Trisha Chakrabarty, LakshmiN. Yatham, Raymond W. Lam in The Canadian Journal of Psychiatry


The personality trait of neuroticism is associated with adverse health outcomes after cancer treatment, but few studies concern men treated for prostate cancer. We examined men with high and low neuroticism treated with radical prostatectomy for curable prostate cancer without relapse. We compared overall problems and domain summary scores (DSSs) between these groups, and if high neuroticism at pre-treatment was a significant predictor of overall problems and DSSs at follow-up. A sample of 462 relapse-free Norwegian men self-rated neuroticism, overall problems, and DSSs by the EPIC-26 before surgery and at three years’ follow-up. Twenty-one percent of the sample had high neuroticism. Patients with high neuroticism reported significantly more overall problems and DSSs at pre-treatment. At follow-up, only overall bowel problems and urinary irritation/obstruction and bowel DSSs were different. High neuroticism was a significant predictor of overall bowel problems and bowel and irritation/obstruction DSSs at follow-up. High neuroticism at pre-treatment was significantly associated with a higher rate of overall problems both at pre-treatment and follow-up and had some significant predictions concerning bowel problems and urinary obstruction at follow-up. Screening for neuroticism at pre-treatment could identify patients in need of more counseling concerning later adverse health outcomes. After cancer therapy, patient-reported outcome measures (PROMs) of dysfunctions and associated problems (bother), often combined as adverse health outcomes (AHOs), have become increasingly popular as a supplement to doctors’ evaluations. Concerning PROMs for men with prostate cancer (PCa), two international working groups have recommended the use of the Expanded Prostate Cancer Index Composite 26-question Short Form (EPIC-26) ,2.PROMs cover the personal subjective health experiences of the patients. These experiences are influenced by their daily health-related activities and their current mental functioning. Personality represents a major element of such functioning, regularly defined as “enduring patterns of perceiving, relating to, and thinking about the environment and oneself” . PersonaIn the general population, a high neuroticism score predisposes to many somatic diseases, mental disorders, and premature death ,9,10. HiDespite its obvious relevance for PCa patients, the relation between neuroticism and AHOs after surgery is covered by few studies. One prospective study by our research group showed that some neuroticism versus no neuroticism at pre-treatment predicted increased sexual bother (problems) one year after radical prostatectomy (RP) . AnotherNo prospective studies known to us of PCa patients treated with RP have examined the impact of pre-treatment high neuroticism on patient-rated overall problems or domain summary scores (DSSs) at pre-treatment and at several years’ follow-up. To fill this gap available pre-treatment and three years’ post-treatment EPIC-26 overall problem ratings and DSSs were analyzed related to high and low levels of neuroticism at pre-treatment. We asked the following research questions: (1) Do men with high versus low neuroticism report more overall problems or significantly lower (worse) DSSs at both time points? (2) Is high neuroticism at pre-treatment a significant predictor of overall problems and DSSs three years after RP?This multicenter study had a longitudinal design and attempted to evaluate AHOs prospectively after RP and radiotherapy. Between November 2008 and December 2009, PCa patients with planned RP were included in the Norwegian Urinary Cancer Group (NUCG) VII study for PCa . Men ageNeuroticism at pre-treatment and the AHOs at several time points were based on responses to a questionnaire. The questionnaire was completed before RP (pre-treatment), and patients completed mailed questionnaires one and three years after RP. In order to study the long-term effects, we only present findings from the pre-treatment and from the three years’ follow-up evaluations.The EPIC-26 is a PROM for rating PCa-related AHOs of the last four weeks covering urinary, bowel, sexual, and hormonal functional domains with four to six items each , relationship status (paired (reference) versus non-paired), work status (paid work versus pensioned), and co-morbidity classified as zero (reference), one, or two or more coexisting somatic disease(s) among those listed in the EPIC-50 [risk groups were defined, and low risk was referenced [Nerve sparing of unilateral or bilateral neurovascular bundles was identified, and no nerve sparing was referenced.The patients rated their  EPIC-50 . Based oferenced . Nerve sDescriptive statistics: Continuous variables were analyzed with t-tests and Mann–Whitney tests in case of skewed distributions. Categorical variables were analyzed with Fisher’s exact tests.Generalized estimation equations (GEEs) were used to identify independent pre-treatment variables that were significant predictors of the rates of overall problems and the DSSs. The GEE is a multivariate binary logistic  or linear regression (DSSs) model of dependent variables at follow-up, examining associations with independent variables assessed at pre-treatment [reatment . Indepenreatment .p-values < 0.05, and all tests were two-sided. The data were analyzed with SPSS for PC version 26 .The level of significance was set to In all, 688 patients had RP, and among them, 675 completed the neuroticism part of the questionnaire at pre-treatment. At three years’ follow-up, 13 patients were deceased, and 551 (83%) delivered new questionnaires. Since biochemical relapse   implied According to our definition 97 men had high neuroticism , and 365 men had low neuroticism.At pre-treatment, the high neuroticism group had significantly higher rates of overall urinary and sexual problems, but not overall bowel problem compared to the low neuroticism group . Significant between-group differences were found for most urinary and sexual items, and for some bowel items. All pre-treatment DSSs were significantly lower (worse) in the high neuroticism group.In our sample, 21% of the men who had RP for PCa reported high neuroticism at pre-treatment. Related to our research questions we first observed that men with high neuroticism at pre-treatment reported significantly higher rates of overall urinary and sexual problems, while the difference for bowel problems was close to significant (At follow-up, those with high neuroticism had significantly more overall bowel problems, while the differences were non-significant for overall urinary and sexual problems and their corresponding items. The high neuroticism group also had lower bowel and urinary irritation/obstruction DSSs. For all overall problems and all DSSs at follow-up, the corresponding measures at pre-treatment were significant positive predictors. High neuroticism at pre-treatment predicted more overall bowel problems and worse bowel and irritation/obstruction DSSs at follow-up.The predictive power of pre-treatment sexual function, age, and nerve sparing for erectile function two years after RP has been demonstrated previously . Pre-treCompared to our previous prospective study of neuroticism in men treated with RP and radiotherapy at 24 months’ follow-up for localized PCA , the preAn interesting finding is that overall sexual problems and most functional sexual issues are significantly more common at pre-treatment in the high neuroticism versus the low neuroticism group. However, at follow-up, these group differences are non-significant. Our tentative explanation is that sexual problems and functions at pre-treatment are mostly determined by psychological factors, i.e., neuroticism and corresponding anxiety and depression . After RIn contrast, the rate of overall bowel problems remained associated with high neuroticism, which then can be viewed as a significant predictor of such problems. The explanation could be that the bowel system hardly is affected by the anatomical changes of RP leaving more influence on high neuroticism. Another explanation could be the fact that bowel function is strongly influenced by mental factors through the so-called “bladder–gut–brain” axis . HoweverOther studies of neuroticism in PCa patients have cross-sectional designs. Perry et al.  demonstrusually behave, feel, or act” in contrast to transient states represented by anxiety and depression based on scores of the last 1–2 weeks. Personality traits are stable cognitive and emotional reaction patterns finally set during childhood and adolescence but somewhat modified later in life [Personality traits such as neuroticism, are rated as “how you  in life . Recentl in life . These fSince high neuroticism is significantly associated with overall problems and DSSs both at pre-treatment and follow-up, clinicians responsible for men after RP should be attentive to high neuroticism as a risk factor for increased problem experience. Eventually, urologists should consider if completion of a short screening PROM for neuroticism see  should bOur study had a considerable sample size, and we also considered the prospective design, use of established PROMs with documented psychometric properties, and use of the GEE statistics as strengths of our study.A limitation of our study was that the participants only rated neuroticism at pre-treatment and not at follow-up. Another limitation was that the psychological data were based on questionnaire responses rather than on psychological evaluation performed by interviews.In conclusion, the current study only weakly supported high neuroticism as a predictor of overall bowel problems and bowel DSS at three years’ follow-up. However, high neuroticism was significantly associated with higher overall problem rates and lower DSSs both at pre-treatment and follow-up. Screening for neuroticism could therefore be helpful for the clinicians in their pre-treatment counseling of PCa patients regarding overall problems and low DSSs after RP.


Salmonella is a major foodborne pathogen in the poultry industry, wherein the control measures may include sanitation and antibacterial and vaccines. However, there have been severe global restrictions on using anti-Salmonella antibacterial agents in livestock. This situation, along with rapidly increasing drug-resistant bacterial species, has led to the exploration of unconventional methods to control Salmonella infection in poultry. In recent years, selection techniques of promising DNA aptamers have begun to permeate several medical branches, resulting in the development of numerous anti-Salmonella DNA aptamers, most of which are used as sensing molecules for diagnostic purposes. These DNA aptamers have been demonstrated to interfere with bacterial growth, multiplication, and viability. Aptamers formed in rolling circle amplification products (RCA-p) could improve the potential action of aptamer interference with bacteria. This study aimed to test the use of single-stranded DNA aptamers in the form of RCA-p as a bacteriostatic to Salmonellain vitro.Salmonella Typhimurium and Salmonella Enteritidis isolates were subjected to the action of anti-ST and anti-SE DNA aptamers in the form of RCA-p. Each isolate was grown on MacConkey and Luria-Bertani agar media separately in different concentrations in the presence or absence of the cognate RCA-p.Salmonella species DNA aptamer-based RCA-p were capable of reducing bacterial growth to significant levels in vitro.The anti-Salmonella in the form of RCA-p could inhibit the bacterial growth rate. Unlike polymerase chain reaction, RCA yields tandem repeats of single-stranded DNA at isothermal conditions, which would increase the probability of receptor-ligand clustering and increase affinity. Furthermore, as our RCA template was bivalent with two DNA aptamer sequences, we could target multiple sites or antigens on a bacterial cell.We describe a potential solution for the rapidly developing drug resistance of several bacterial species. Our findings suggested that the use of non-toxic, non-immunogenic, and low-cost DNA aptamers targeting  Salmonella remains the major bacterial species among foodborne pathogens. Salmonella Typhimurium (ST) and Salmonella Enteritidis (SE) are the primary species among foodborne contaminants worldwide . In all data sets, mean±standard deviation and p-value were the primary comparison elements. The Tukey honestly significant difference test was used to determine the significant differences among mean values at p<0.01.Statistical analyses of bacterial growth were performed using multiway analysis of variance (ANOVA) to demonstrate variation between treatments using the general linear model procedure of the SAS software . Th. Th14]. The process of RCA production involves several enzymatic treatments in exchange with DNA purification and reconcentration. Therefore, several controls must be included to exclude errors . Our res−1, 1×10−2, and 1×10−3 CFU) because of non-countable, very dense bacterial growth at these concentrations. These densely growing bacteria were identical for both bacterial isolates and on both agar media.We could not include the results obtained using very high bacterial concentrations . For the ST isolate, the results demonstrated a significant reduction in bacterial growth (p>0.01) in all samples, except at the concentration of 1×10−6. Similarly, for the SE isolate, the growth was significantly reduced by the addition of RCA-p at the high bacterial concentration (Figures-After mixing with RCA-p, 100 μL of both ST and SE isolates was inoculated on LB agar in tenfold serial dilutions. RCA-p were added in a constant concentration (~100 μg.mL Figures- and 3. NOn MacConkey agar, the growth rate of the bacterial isolates was consistent with that on LB agar. For both bacterial isolates, RCA-p addition resulted in significantly reduced growth at only the higher bacterial concentrations Figures- and 5. TAs we did not prepare a standard McFarland bacterial suspension, the precision of the bacterial inhibition result was not highly accurate. In this, we simulated the disk diffusion assay ,16.We confirmed that DNA aptamers, in the form of RCA, were capable of reducing bacterial growth. However, lower concentrations of the bacteria in the samples had no effect, which could be due to the low concentration of RCA as we had no method to calibrate the number of bacteria in the sample with the concentration of RCA. The significance of our study is that RCA-p are very good candidates that could replace traditional antibacterial agents. However, this technique may require a few adjustments to sustain the ability and practicality necessary for commercial requirements.ASA and SSH: Conceived and designed the experiment and conducted and analyzed the data. ASA, SSH, and NN: Contributed to the manuscript drafting and revisions. All authors have read and approved the final manuscript.