Molecular modeling study combined with deep learning approach for the identification of potent β-catenin inhibitors

Abstract

β-catenin is a propitious target for various cancer drugs for inhibiting tumour cell proliferation and differentiation. Even though several inhibitors have been discovered for β-catenin but its selectivity towards β-catenin and TCF-4 interactions is a major challenge. Hence, the expedition for identifying a selective drug for β-catenin inhibition against cancer will have immense potential and favour. The present study aims to scrutinise compounds that can impede β-catenin overexpression in cancer using an integrated pharmacophore and in silico docking-based screening of 28,007 molecules from the ZINC repository. The analysis yielded the top two compounds, namely ZINC000016051423 and ZINC000028564770, with better docking scores of -4.007 kcal/mol and -6.547 kcal/mol at the β-catenin binding pocket. Moreover, their free energy scores were -40.882 and -53.989 kcal/mol with favourable drug-likeness characteristics. Eventually, both hits exhibited better inhibitory activity against 66 colorectal cell lines using the PaccMann algorithm. In conclusion, our findings suggest that the lead compounds may serve as a possible β-catenin inhibitor during the treatment of cancer, though further experimental study is needed to evaluate the compound’s efficacy.