Discovery of potential ALK inhibitors from Glycosmis phytochemicals via integrated machine learning and umbrella sampling approaches
5 affiliations
- 1Institute of Chemistry, Vietnam Academy of Science and Technology (VAST) 1 , 18 Hoang Quoc Viet, Nghia Do, Hanoi, Vietnam
- 2Department of Chemistry, Vinh University 2 , 182 Le Duan, Truong Vinh, Nghean 43000, Vietnam
- 3Faculty of Chemistry and Center for Computational Science, Hanoi National University of Education 3 , 136 Xuan Thuy, Hanoi 10000, Vietnam
- 4Institute of Materials Science, VAST 4 , 18 Hoang Quoc Viet, Hanoi 10000, Vietnam
- 5Institute of Biology, Chemistry and Environment, Vinh University 5 , 182 Le Duan, Truong Vinh, Nghean 43000, Vietnam
Abstract
Anaplastic lymphoma kinase (ALK) is a key therapeutic target for various cancers because of its central role in tumor growth and survival signaling. This study employed an integrated computational workflow combining machine learning (ML), molecular docking, molecular dynamics (MD) simulations, umbrella sampling (US), and toxicity prediction to identify potential ALK inhibitors from 307 phytochemicals of the Glycosmis genus. A previously validated CatBoost regression model predicted binding free energies, leading to the selection of 26 compounds with ΔGML < −11 kcal mol−1. Molecular docking identified six top candidates with favorable binding affinities ranging from −5.856 to −9.667 kcal mol⁻¹, which subsequently underwent 200 ns MD simulations. Structural analyses confirmed the stability of all ALK–ligand complexes, while interaction studies revealed extensive hydrophobic contacts and conserved hydrogen bonds involving key ALK residues. US calculations estimated binding free energies from −8.354 to −13.272 kcal mol−1, with compound 54 exhibiting the strongest affinity (ΔGUS = −13.272 kcal mol−1). Several alkaloids, including acrifoline, heptazoline, and atalaphyllidine, also demonstrated promisingbinding properties and reported anticancer activities. Toxicity prediction indicated acceptable safety profiles, highlighting Glycosmis-derived phytochemicals as promising potential ALK inhibitor candidates and supporting the utility of ML-guided virtual screening combined with advanced molecular simulations for natural product-based anticancer drug discovery. Nevertheless, biochemical and cellular experiments are required to confirm their ALK inhibitory activity, selectivity, and therapeutic potential.
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BibTeX
@article{nguyen2026discovery,
title = {{Discovery of potential ALK inhibitors from Glycosmis phytochemicals via integrated machine learning and umbrella sampling approaches}},
author = {Xuan Ha Nguyen and Xuan Duc Dau and Thi Minh Hue Nguyen and Ngoc Dung Tran and Van Trang Nguyen and Le Binh Doan and Thi My Chau Le and Thuy Phan Thi},
journal = {Royal Society Open Science},
year = {2026},
month = nov,
doi = {10.1098/rsos.261178},
url = {https://royalsocietypublishing.org/rsos/article/13/11/261178/484015/Discovery-of-potential-ALK-inhibitors-from},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Discovery of potential ALK inhibitors from Glycosmis phytochemicals via integrated machine learning and umbrella sampling approaches
AU - Xuan Ha Nguyen
AU - Xuan Duc Dau
AU - Thi Minh Hue Nguyen
AU - Ngoc Dung Tran
AU - Van Trang Nguyen
AU - Le Binh Doan
AU - Thi My Chau Le
AU - Thuy Phan Thi
PY - 2026
DA - 2026/11/04
JO - Royal Society Open Science
DO - 10.1098/rsos.261178
UR - https://royalsocietypublishing.org/rsos/article/13/11/261178/484015/Discovery-of-potential-ALK-inhibitors-from
AB - Anaplastic lymphoma kinase (ALK) is a key therapeutic target for various cancers because of its central role in tumor growth and survival signaling. This study employed an integrated computational workflow combining machine learning (ML), molecular docking, molecular dynamics (MD) simulations, umbrella sampling (US), and toxicity prediction to identify potential ALK inhibitors from 307 phytochemicals of the Glycosmis genus. A previously validated CatBoost regression model predicted binding free energies, leading to the selection of 26 compounds with ΔGML < −11 kcal mol−1. Molecular docking identified six top candidates with favorable binding affinities ranging from −5.856 to −9.667 kcal mol⁻¹, which subsequently underwent 200 ns MD simulations. Structural analyses confirmed the stability of all ALK–ligand complexes, while interaction studies revealed extensive hydrophobic contacts and conserved hydrogen bonds involving key ALK residues. US calculations estimated binding free energies from −8.354 to −13.272 kcal mol−1, with compound 54 exhibiting the strongest affinity (ΔGUS = −13.272 kcal mol−1). Several alkaloids, including acrifoline, heptazoline, and atalaphyllidine, also demonstrated promisingbinding properties and reported anticancer activities. Toxicity prediction indicated acceptable safety profiles, highlighting Glycosmis-derived phytochemicals as promising potential ALK inhibitor candidates and supporting the utility of ML-guided virtual screening combined with advanced molecular simulations for natural product-based anticancer drug discovery. Nevertheless, biochemical and cellular experiments are required to confirm their ALK inhibitory activity, selectivity, and therapeutic potential.
SN - 2054-5703
ER -