Integrating machine learning-based molecular design with experimental validation for the discovery of EGFR inhibitors in lung cancer
- 1Department of Thoracic Surgery, Affiliated Hospital of Hebei University, Baoding, 071000 Hebei China
- 2Department of Tuberculosis, Affiliated Hospital of Hebei University, Baoding, 071000 Hebei China
- 3School of Science, China Pharmaceutical University, Nanjing, 211100 Jiangsu China
Abstract
The emergence of drug resistance and off-target toxicities in epidermal growth factor receptor (EGFR) targeted therapies underscores the urgent need for novel inhibitor scaffolds. This study integrates artificial intelligence-driven generative models with experimental validation to discover novel, selective EGFR inhibitors. Utilizing REINVENT4, a reinforcement learning-based generative framework, we performed a stage-wise, multi-objective optimization using a curated dataset of active EGFR inhibitors. The optimization was guided by a composite reward function incorporating docking scores, and quantitative estimates of drug-likeness (QED) and synthetic accessibility (SAscore). Candidate molecules were subsequently evaluated using molecular dynamics (MD) simulations, synthesized, and subjected to in vitro kinase and cellular assays. The generative pipeline successfully converged on a promising N-(quinolin-5-yl) benzenesulfonamide scaffold. Among the synthesized candidates, Hit1 exhibited potent in vitro EGFR kinase inhibition (IC50 = 21.22 nM), although ~ 19-fold less potent than Gefitinib. MD simulations analyses revealed that hydrogen bond interactions with Lys745 and proper occupation of the Val726 hydrophobic cavity are critical for binding. Notably, Hit1 demonstrated robust, targeted anti-proliferative activity against EGFR-mutant non-small cell lung cancer (NSCLC) cells (PC9 and HCC827), while displaying strong selectivity over wild-type EGFR cells (A549). Our findings validate the efficacy of a target-aware reinforcement learning approach for de novo drug design. The discovered quinoline-sulfonamide derivative represents a highly promising, synthetically tractable lead compound for the development of next-generation mutation-selective EGFR inhibitors.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s11030-026-11625-z.
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BibTeX
@article{qie2026integrating,
title = {{Integrating machine learning-based molecular design with experimental validation for the discovery of EGFR inhibitors in lung cancer}},
author = {Hailing Qie and Liyuan Wang and Ce Li and Chen Wu and Yong Wang and Kuo Xiao and Lili Diao},
journal = {Molecular Diversity},
year = {2026},
month = jun,
doi = {10.1007/s11030-026-11625-z},
url = {https://doi.org/10.1007/s11030-026-11625-z},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Integrating machine learning-based molecular design with experimental validation for the discovery of EGFR inhibitors in lung cancer
AU - Hailing Qie
AU - Liyuan Wang
AU - Ce Li
AU - Chen Wu
AU - Yong Wang
AU - Kuo Xiao
AU - Lili Diao
PY - 2026
DA - 2026/06/15
JO - Molecular Diversity
DO - 10.1007/s11030-026-11625-z
UR - https://doi.org/10.1007/s11030-026-11625-z
AB - The emergence of drug resistance and off-target toxicities in epidermal growth factor receptor (EGFR) targeted therapies underscores the urgent need for novel inhibitor scaffolds. This study integrates artificial intelligence-driven generative models with experimental validation to discover novel, selective EGFR inhibitors. Utilizing REINVENT4, a reinforcement learning-based generative framework, we performed a stage-wise, multi-objective optimization using a curated dataset of active EGFR inhibitors. The optimization was guided by a composite reward function incorporating docking scores, and quantitative estimates of drug-likeness (QED) and synthetic accessibility (SAscore). Candidate molecules were subsequently evaluated using molecular dynamics (MD) simulations, synthesized, and subjected to in vitro kinase and cellular assays. The generative pipeline successfully converged on a promising N-(quinolin-5-yl) benzenesulfonamide scaffold. Among the synthesized candidates, Hit1 exhibited potent in vitro EGFR kinase inhibition (IC50 = 21.22 nM), although ~ 19-fold less potent than Gefitinib. MD simulations analyses revealed that hydrogen bond interactions with Lys745 and proper occupation of the Val726 hydrophobic cavity are critical for binding. Notably, Hit1 demonstrated robust, targeted anti-proliferative activity against EGFR-mutant non-small cell lung cancer (NSCLC) cells (PC9 and HCC827), while displaying strong selectivity over wild-type EGFR cells (A549). Our findings validate the efficacy of a target-aware reinforcement learning approach for de novo drug design. The discovered quinoline-sulfonamide derivative represents a highly promising, synthetically tractable lead compound for the development of next-generation mutation-selective EGFR inhibitors. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s11030-026-11625-z.
SN - 1381-1991
SN - 1573-501X
ER -