ABSTRACT
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.
Subject(s)
Agrochemicals/chemistry , Computational Biology/methods , Drug Design , Enzyme Inhibitors/chemistry , Enzyme Inhibitors/pharmacology , Pharmaceutical Preparations/chemistry , Pharmaceutical Preparations/classification , Algorithms , Artificial Intelligence , Carbonic Anhydrase Inhibitors/chemistry , Carbonic Anhydrase Inhibitors/pharmacology , Databases as Topic , Forecasting , Molecular Conformation , Nonlinear Dynamics , Quantitative Structure-Activity Relationship , Terminology as TopicABSTRACT
Some modifications were introduced into the previously described Centroid diversity sorting algorithm, which uses cosine similarity metric. The modified algorithm is suitable for the work with large databases on personal computers. For example, for diversity sorting of the database with the size greater than a million of records, less than 9 h are required (Pentium III, 800 MHz). The problem of selecting new compounds into the existing collection is examined to reach the maximum diversity of the collection. The article describes the new algorithm for the selection of heterocyclic compounds.
