Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

ABSTRACT:

Background

Epidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung cancer (NSCLC) patients, leading to drug resistance and disease progression. Identification of new selective EGFR-T790M inhibitors has proven challenging through traditional screening platforms. With great advances in computer algorithms, machine learning improved the screening rates of molecules at full chemical spaces, and these molecules will present higher biological activity and targeting efficiency.

Methods

An integrated machine learning approach, integrated by Bayesian inference, was employed to screen a commercial dataset of 70,413 molecules, identifying candidates that selectively and efficiently bind with EGFR harboring T7

SUBMITTER: Zhou R 

PROVIDER: S-EPMC11619116 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature