Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach.

ABSTRACT: Double mutated epidermal growth factor receptor is a clinically important target for addressing drug resistance in lung cancer treatment. Therefore, discovering new inhibitors against the T790M/L858R (TMLR) resistant mutation is ongoing globally. In the present study, nearly 150 000 molecules from various natural product libraries were screened by employing different ligand and structure-based techniques. Initially, the library was filtered to identify drug-like molecules, which were subjected to a machine learning based classification model to identify molecules with a higher probability of having anti-cancer activity. Simultaneously, rules for constrained docking were derived from three-dimensional protein-ligand complexes and thereafter, constrained docking was undertaken, followed by H

SUBMITTER: Agarwal SM 

PROVIDER: S-EPMC9170516 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature