Chemoinformatics in anti-cancer chemotherapy: multi-target QSAR model for the in silico discovery of anti-breast cancer agents

Abstract

The discovery of new and more efficient anti-cancer chemotherapies is a field of research in expansion and growth. Breast cancer (BC) is one of the most studied cancers because it is the principal cause of cancer deaths in women. In the active area for the search of more potent anti-BC drugs, the use of approaches based on Chemoinformatics has played a very important role. However, until now there is no methodology able to predict anti-BC activity of compounds against more than one BC cell line, which should constitute a greater interest. In this study we introduce the first chemoinformatic multi-target (mt) approach for the in silico design and virtual screening of anti-BC agents against 13 cell lines. Here, an mt-QSAR discriminant model was developed using a large and heterogeneous database of compounds. The model correctly classified 88.47% and 92.75% of active and inactive compounds respectively, in training set. The validation of the model was carried out by using a prediction set which showed 89.79% of correct classification for active and 92.49% for inactive compounds. Some fragments were extracted from the molecules and their contributions to anti-BC activity were calculated. Several fragments were identified as potential substructural features responsible for anti-BC activity and new molecules designed from those fragments with positive contributions were suggested as possible potent and versatile anti-BC agents.