title: Ligand efficiency-based support vector regression models for predicting bioactivities of ligands to drug target proteins.
abstract: The concept of ligand efficiency (LE) indices is widely accepted throughout the drug design community and is frequently used in a retrospective manner in the process of drug development. For example, LE indices are used to investigate LE optimization processes of already-approved drugs and to re-evaluate hit compounds obtained from structure-based virtual screening methods and/or high-throughput experimental assays. However, LE indices could also be applied in a prospective manner to explore drug candidates. Here, we describe the construction of machine learning-based regression models in which LE indices are adopted as an end point and show that LE-based regression models can outperform regression models based on pIC50 values. In addition to pIC50 values traditionally used in machine learning studies based on chemogenomics data, three representative LE indices (ligand lipophilicity efficiency (LLE), binding efficiency index (BEI), and surface efficiency index (SEI)) were adopted, then used to create four types of training data. We constructed regression models by applying a support vector regression (SVR) method to the training data. In cross-validation tests of the SVR models, the LE-based SVR models showed higher correlations between the observed and predicted values than the pIC50-based models. Application tests to new data displayed that, generally, the predictive performance of SVR models follows the order SEI > BEI > LLE > pIC50. Close examination of the distributions of the activity values (pIC50, LLE, BEI, and SEI) in the training and validation data implied that the performance order of the SVR models may be ascribed to the much higher diversity of the LE-based training and validation data. In the application tests, the LE-based SVR models can offer better predictive performance of compound-protein pairs with a wider range of ligand potencies than the pIC50-based models. This finding strongly suggests that LE-based SVR models are better than pIC50-based models at predicting bioactivities of compounds that could exhibit a much higher (or lower) potency.
authors.affiliation: Drug Discovery Department, Research & Development Division, PharmaDesign, Inc. , Hatchobori 2-19-8, Chuo-ku, Tokyo 104-0032, Japan.
authors.forename: Nobuyoshi
authors.initials: N
authors.lastname: Sugaya
mesh_terms: D001665:Binding Sites; D062126:Databases, Chemical; D015507:Drugs, Investigational / Q000737:chemistry*; D057166:High-Throughput Screening Assays; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D020128:Inhibitory Concentration 50; D007473:Ion Channels / Q000819:agonists / Q000037:antagonists & inhibitors / Q000737:chemistry*; D008024:Ligands; D016015:Logistic Models; D008968:Molecular Conformation; D011237:Predictive Value of Tests; D011485:Protein Binding; D011494:Protein Kinases / Q000737:chemistry*; D011957:Receptors, Opioid / Q000819:agonists / Q000737:chemistry*; D012107:Research Design; D054852:Small Molecule Libraries / Q000737:chemistry*; D013329:Structure-Activity Relationship; D060388:Support Vector Machine; D014584:User-Computer Interface
chemical_list: D015507:Drugs, Investigational; C091041:G protein-coupled receptor C3; D007473:Ion Channels; D008024:Ligands; D011957:Receptors, Opioid; D054852:Small Molecule Libraries; D011494:Protein Kinases