In-Silico Predictions of Drug Resistance in Lung Cancers with EGFR Mutation

Cancer treatment is often hindered by the emergence of drug resistance, frequently driven by novel mutations in oncogenes or drug-targeted pathways. Predicting resistance mechanisms is critical for informing therapeutic strategies and improving patient outcomes. Here, we present a computational workflow that leverages high-performance computing (HPC) resources to systematically evaluate the impact of emerging mutations on drug efficacy. Our workflow integrates deep learning structure prediction, molecular dynamics simulations, molecular docking, and binding predictions of known compounds to predict resistance mechanisms and propose alternative therapeutic options. We also explore quantum chemical calculations as a tool to complement experimental validations to better understand the binding preferences between different protein forms.