Cancers
Machine Learning-Guided Virtual Screening Identifies a Novel MELK-Targeted Antiproliferative Lead Compound with G2/M Arrest Activity in Triple-Negative Breast Cancer and Lung Adenocarcinoma Cells
By Cao W, Jiang B, Chi X and 9 others.
Abstract
Background
Maternal embryonic leucine zipper kinase (MELK) is a cell cycle-regulated serine/threonine kinase frequently overexpressed in human malignancies, and represents a promising anticancer drug target. Discovery of structurally novel, highly selective MELK inhibitors remains an unmet need.
Methods
In the study, we implemented an integrated computational pipeline combining four machine learning regressors (Random Forest, Gradient Boosting, XGBoost, LightGBM), hierarchical Glide docking (HTVS to SP to XP), MM/GBSA rescoring, ADMET prediction, and 100 ns molecular dynamics simulations to screen a ~15.58 million-compound commercial library, yielding 12 prioritized candidates. Three commercially available compounds (CPD4, CPD6, CPD10) were evaluated in an antiproliferative panel.