Ligand- and structural-based discovery of potential small molecules that target the colchicine site of tubulin for cancer treatment.

Small molecules targeting the colchicine site of tubulin represent an attractive cancer treatment strategy. In this study, a total of 468 models derived from 1076 diverse inhibitors binding to the tubulin colchicine site were constructed based on fingerprints using three machine learning approaches: 1) naive Bayesian (NB); 2) single tree (ST); and 3) random forest (RF). The overall predictive accuracy of the best models exceeded 85.12% for both the training and test sets. We designed an integrated virtual screening (VS) strategy for identifying new tubulin inhibitors by combining established models, molecular docking, and similarity-based analog searching. Through two rounds of VS, compound 23g was identified as a novel potent anticancer agent exhibiting activity against MDA-MB-231, HeLa, A549, HepG2, CNE2, and HCT116 tumor cell lines with IC

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Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.