Machine learning-based QSAR and structure-based virtual screening guided discovery of novel mIDH1 inhibitors from natural products. (PubMed, J Comput Aided Mol Des)
The QSAR model predictions indicate that the hit compounds have high binding affinity to the target protein, and its pIC50 value was found to be considerably larger than that of AGI-5198...Furthermore, the binding free energy decomposition and per-residue contribution of the IDH1R132H-inhibitor complex revealed key fragments of the inhibitor interacting with residues ALA-111, PRO-118, ARG-119, LE-128, ILE-130, ITRP-267, VAL-281, and TYR-285 in the binding site of IDH1R132H. This investigation indicates that CNP0047068, CNP0029964, and CNP0025598 have the potential to be targeted inhibitors of IDH1R132H mutants through further optimization, providing new insights for discovering novel lead scaffolds in this domain.