Similarity boosting for label noise tolerance in protein-chemical interaction prediction
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Globalized bipartite local model for drug-target interaction prediction
Proceedings of the 11th International Workshop on Data Mining in Bioinformatics
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Functional characterization of drug-protein interactions network
Intelligent Data Analysis
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Motivation: Predicting interactions between small molecules and proteins is a crucial step to decipher many biological processes, and plays a critical role in drug discovery. When no detailed 3D structure of the protein target is available, ligand-based virtual screening allows the construction of predictive models by learning to discriminate known ligands from non-ligands. However, the accuracy of ligand-based models quickly degrades when the number of known ligands decreases, and in particular the approach is not applicable for orphan receptors with no known ligand. Results: We propose a systematic method to predict ligand–protein interactions, even for targets with no known 3D structure and few or no known ligands. Following the recent chemogenomics trend, we adopt a cross-target view and attempt to screen the chemical space against whole families of proteins simultaneously. The lack of known ligand for a given target can then be compensated by the availability of known ligands for similar targets. We test this strategy on three important classes of drug targets, namely enzymes, G-protein-coupled receptors (GPCR) and ion channels, and report dramatic improvements in prediction accuracy over classical ligand-based virtual screening, in particular for targets with few or no known ligands. Availability: All data and algorithms are available as Supplementary Material. Contact: laurent.jacob@ensmp.fr Supplementary information:Supplementary data are available at Bioinformatics online.