Combinatorial networks.

Abstract

A novel approach for the analysis and virtual screening of large combinatorial libraries is presented. The method attempts to relieve the computational burden by computing the properties of the products in a way that does not require their explicit enumeration. In particular, a small subset of compounds from the virtual library is identified and their descriptors are calculated in a conventional manner. The resulting data is used as input to a multilayer perceptron, which is trained to predict the descriptors of the products from the descriptors of their respective building blocks. Once trained, the neural network is able to estimate the descriptors of the remaining members of the virtual library with remarkable accuracy, without ever, generating their connection tables. This method eliminates the two most time-consuming steps in virtual screening and allows the processing of very large combinatorial libraries that are intractable with conventional techniques.