Recognition of Chemical Classes and Drug Design

Muller et al. investigated several machine learning algorithms for their ability to identify drug-like compounds based on a set of atom type counts. Five machine learning procedures were investigated SVM with polynomial 

Svetnik et al. performed a large-scale evaluation of the stochastic gradient boosting method (SGB), which implements a jury of classification and 

Selecting an optimum group of descriptors is both an important and time-consuming phase in developing a predictive QSAR model. Frohlich, Wegner, and Zell introduced the incremental regularized risk minimization procedure for SVM classification and regression models, and they compared it with recursive feature elimination and with the mutual information procedure. Their first experiment considered 164 compounds that had been tested for their human intestinal absorption, whereas the second experiment modeled the aqueous solubility prediction for 1297 compounds. Structural descriptors were computed by those authors with JOELib and MOE, and full cross-validation was performed to compare the descriptor selection methods. The incremental 

Five methods of feature selection (information gain, mutual information, X -test, odds ratio, and GSS coefficient) were compared by Liu for their ability to discriminate between thrombin inhibitors and noninhibitors.The chemical compounds were provided by DuPont Pharmaceutical Research Laboratories as a learning set of 1909 compounds contained 42 inhibitors and 1867 noninhibitors, and a test set of 634 compounds contained 150 inhibitors and 484 noninhibitors. Each compound was characterized by 139,351 binary features describing their 3-D structure. In this comparison of naive Bayesian and SVM classifiers, all compounds were considered together, and a L10%O cross-validation procedure was applied. Based on information gain descriptor selection, 

SVM was robust to a 99% reduction of the descriptor space, with a small decrease in sensitivity (from 58.7% to 52.5%) and specificity (from 98.4% to 97.2%). 

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