Identification of novel potential hypoxia-inducible factor-1α inhibitors through machine learning and computational simulations.

ABSTRACT:

Introduction

Hypoxia-inducible factor-1α (HIF-1α) has become a significant therapeutic target for breast cancer and other cancers by regulating the expression of downstream genes such as erythropoietin, thereby improving cell survival in hypoxic conditions.

Methods

We jointly applied a multistage screening system encompassing machine learning, molecular docking, and molecular dynamics simulations to conduct virtual screening of the "Traditional Chinese Medicine Monomer Library" for potential HIF-1α inhibitors. The virtual screening was conducted in three sequential stages, applying the following selection criteria sequentially: an activity prediction score greater than or equal to 0.8, a stronger binding affinity, and an MM-PBSA binding free energy lower than the reference c

SUBMITTER: He Y 

PROVIDER: S-EPMC12104167 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature