A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models

Combretastatin-like chalcones are promising anticancer compounds that inhibit the mitotic process through interactions with β-tubulin. A detailed study of these compounds can contribute for the rational drug design of new structures aiming at compounds with high biological activity. For this purpose, we have studied 87 combretastatin-like chalcones and proposed multivariate models based on partial least squares (PLS), artificial neural network consensus model (ANN-CM), and general consensus model (GCM). The proposed models have showed good predictive ability with r2test = 0.812 and MSE (test set) = 0.327 for the PLS model, r2test = 0.829 and MSE (test set) = 0.286 for the ANN-CM, and r2test = 0.822 and and MSE (test set) = 0.302 for the GCM. The selected molecular and electronic descriptors (RDF045e, RTv, RDF155u, RDF035m, SP02, PI, UNIP and EHOMO-3) represent molecular features of the compounds that can be associated to the biological activity and can be employed to help the design of new bioactive ligands with improved biological activity.