Predicting network of drug-enzyme interaction based on machine learning method.
It is important to correctly and efficiently map drugs and enzymes to their possible interaction network in modern drug research. In this work, a novel approach was introduced to encode drug and enzyme molecules with physicochemical molecular descriptors and pseudo amino acid composition, respectively. Based on this encoding method, Random Forest was adopted to build the drug-enzyme interaction network. After selecting the optimal features that are able to represent the main factors of drug-enzyme interaction in our prediction, a total of 129 features were attained which can be clustered into nine categories: Elemental Analysis, Geometry, Chemistry, Amino Acid Composition, Secondary Structure, Polarity, Molecular Volume, Codon Diversity and Electrostatic Charge. It is further found that Geometry features were the most important of all the features. As a result, our predicting model achieved an MCC of 0.915 and a sensitivity of 87.9% at the specificity level of 99.8% for 10-fold cross-validation test, and achieved an MCC of 0.895 and a sensitivity of 95.7% at the specificity level of 95.4% for independent set test. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.
['Niu|Bing|B|', 'Zhang|Yuchao|Y|', 'Ding|Juan|J|', 'Lu|Yin|Y|', 'Wang|Miao|M|', 'Lu|Wencong|W|', 'Yuan|Xiaochen|X|', 'Yin|Jinyuan|J|']
D000465:Algorithms D001185:Artificial Intelligence D019295:Computational Biology D015195:Drug Design D004791:Enzyme Inhibitors D006801:Humans D060066:Protein Interaction Maps D011506:Proteins D020539:Sequence Analysis, Protein
Machine learning Molecular descriptor Pseudo amino acid composition Random Forest CfsSubset Drug–enzyme interaction