Predicting the Impact of Drug Resistance Mutations on Inhibitor Potency with Molecular Dynamics and Machine Learning

Many enzymes are vital drug targets in diseases such as cancer and pathogenic infections; however, mutations can drastically disrupt inhibitor binding to confer resistance. Resistance mutations primarily occur at the inhibitor binding site, but accompanying distal mutations can exacerbate resistance...

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Bibliografski detalji
Glavni autor:	Lauren E. Intravaia (22438658) (author)
Daljnji autori:	Ala M. Shaqra (6207860) (author), Somayeh Pirhadi (4693132) (author), Noa Popko (22438661) (author), Nese Kurt Yilmaz (7229123) (author), Celia A. Schiffer (148545) (author)
Izdano:	2025
Teme:	Biophysics Biochemistry Genetics Pharmacology Biotechnology Cancer Hematology Infectious Diseases Virology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified mutated enzyme variant active site sufficient vital drug targets inhibitor binding site accompanying distal mutations binding potency due drug resistance mutations unseen drug drug design potent inhibitor inhibitor potency resistance mutations sequences alone robustly predict pathogenic infections often critical molecular dynamics models based intramolecular interactions feature selection far better experimental value exacerbate resistance confer resistance based features based analysis accurate method 28 variants 1 protease
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Predicting the Impact of Drug Resistance Mutations on Inhibitor Potency with Molecular Dynamics and Machine Learning

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