Stochastic versus Stepwise Strategies for Quantitative Structure−Activity Relationship GenerationHow Much Effort May the Mining for Successful QSAR Models Take?†

Descriptor selection in QSAR typically relies on a set of upfront working hypotheses in order to boil down the initial descriptor set to a tractable size. Stepwise regression, computationally cheap and therefore widely used in spite of its potential caveats, is most aggressive in reducing the effectively explored problem space by adopting a greedy variable pick strategy. This work explores an antipodal approach, incarnated by an original Genetic Algorithm (GA)-based Stochastic QSAR Sampler (SQS) that favors unbiased model search over computational cost. Independent of a priori descriptor filtering and, most important, not limited to linear models only, it was benchmarked against the ISIDA Stepwise Regression (SR) tool. SQS was run under various premises, varying the training/validation set splitting scheme, the nonlinearity policy, and the used descriptors. With the considered three anti-HIV compound sets, repeated SQS runs generate sometimes poorly overlapping but nevertheless equally well validating mod...