Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions.
Support Vector Machines (SVM) is a powerful classification and regression tool that is becoming increasingly popular in various machine learning applications. We tested the ability of SVM, in comparison with well-known neural network techniques, to predict drug-likeness and agrochemical-likeness for large compound collections. For both kinds of data, SVM outperforms various neural networks using the same set of descriptors. We also used SVM for estimating the activity of Carbonic Anhydrase II (CA II) enzyme inhibitors and found that the prediction quality of our SVM model is better than that reported earlier for conventional QSAR. Model characteristics and data set features were studied in detail.
Zernov
Vladimir V
VV
Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1, 119899 GSP-3 Moscow, Russia.
Balakin
Konstantin V
KV
Ivaschenko
Andrey A
AA
Savchuk
Nikolay P
NP
Pletnev
Igor V
IV
D016573:Agrochemicals / Q000737:chemistry*; D000465:Algorithms; D001185:Artificial Intelligence; D002257:Carbonic Anhydrase Inhibitors / Q000737:chemistry / Q000494:pharmacology; D019295:Computational Biology / Q000379:methods*; D019992:Databases as Topic; D015195:Drug Design; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D005544:Forecasting; D008968:Molecular Conformation; D017711:Nonlinear Dynamics; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000145:classification*; D021281:Quantitative Structure-Activity Relationship; D009626:Terminology as Topic
D016573:Agrochemicals; D002257:Carbonic Anhydrase Inhibitors; D004791:Enzyme Inhibitors; D004364:Pharmaceutical Preparations