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DTSTAMP:20250822T115805Z
LOCATION:Room 5.0A52
DTSTART;TZID=Europe/Stockholm:20250616T173000
DTEND;TZID=Europe/Stockholm:20250616T180000
UID:submissions.pasc-conference.org_PASC25_sess166_pap130@linklings.com
SUMMARY:In-Silico Predictions of Drug Resistance in Lung Cancers with Egfr
  Mutation
DESCRIPTION:Ibrahim Imam, Usman Abbas, Christian Gosser, and Christine Bra
 inson (University of Kentucky); WA de Jong (Lawrence Berkeley National Lab
 oratory); and Xiaoqi Liu, Hunter Moseley, Shao Qing, Shulin Zhang, Ralph Z
 inner, and Sally Ellingson (University of Kentucky)\n\nCancer treatment is
  often hindered by the emergence of drug resistance, frequently driven by 
 novel mutations in oncogenes or drug-targeted pathways. Predicting resista
 nce mechanisms is critical for informing therapeutic strategies and improv
 ing patient outcomes. Here, we present a computational workflow that lever
 ages high-performance computing (HPC) resources to systematically evaluate
  the impact of emerging mutations on drug efficacy. Our workflow integrate
 s deep learning structure prediction, molecular dynamics simulations, mole
 cular docking, and binding predictions of known compounds to predict resis
 tance mechanisms and propose alternative therapeutic options. We also expl
 ore quantum chemical calculations as a tool to complement experimental val
 idations to better understand the binding preferences between different pr
 otein forms.\n\nDomain: Chemistry and Materials, Life Sciences\n\nSession 
 Chair: Bertil Schmidt (Johannes Gutenberg University Mainz)\n\n
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