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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 HYDE binding affinity assessment. As a result, three molecules that resemble interactions similar to the co-crystallized complex were selected and subjected to 100 ns molecular dynamics simulation for stability analysis. The interaction analysis for the 100 ns simulation period showed that the leads exhibit the conserved hydrogen bond interaction with Gln791 and Met793 as in the co-crystal ligand. Also, the study indicated that Y-shaped molecules are preferred in the binding pocket as it enables them to occupy both pockets. The MMGBSA binding energy calculations revealed that the molecules have comparable binding energy to the native ligand. The present study has enabled the identification of a few ADMET adherent leads from natural products that exhibit the potential to inhibit the double mutated drug-resistant EGFR.

rsc advances

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