A methodology is presented in which high throughput screening experimental data are used to construct a probabilistic QSAR model which is subsequently used to select building blocks for a virtual combinatorial library. The methodology is based upon statistical probability estimation and not regression. The methodology is applied to the construction of two focused virtual combinatorial libraries: one for cyclic GMP phosphodiesterase type V inhibitors and one for acyl-CoA:cholesterol O-acyltransferase inhibitors. The results suggest that the methodology is capable of selecting combinatorial substituents that lead to active compounds starting with binary (pass / fail) activity measurements.
Keywords: high throughput drug discovery, acat, cholesterol O-acyltransferase(acat)
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