RESEARCH ARTICLE

Synergistic application of molecular docking and machine learning for improved binding pose

¹ College of Life Sciences, Hunan Normal University, Changsha 410006, China
² DP Technology, Beijing 100089, China
³ Gaoling School of Artificial Intelligence, Remin University of China, Beijing 100872, China
⁴ AI for Science Institute, Beijing 100080, China

^* Corresponding authors (emails: zhanglf@dp.tech (Linfeng Zhang); zhengh@dp.tech (Hang Zheng))

Received: 10 September 2023
Revised: 1 March 2024
Accepted: 5 March 2024

Abstract

Accurate prediction of protein-ligand complex structures is a crucial step in structure-based drug design. Traditional molecular docking methods exhibit limitations in terms of accuracy and sampling space, while relying on machine-learning approaches may lead to invalid conformations. In this study, we propose a novel strategy that combines molecular docking and machine learning methods. Firstly, the protein-ligand binding poses are predicted using a deep learning model. Subsequently, position-restricted docking on predicted binding poses is performed using Uni-Dock, generating physically constrained and valid binding poses. Finally, the binding poses are re-scored and ranked using machine learning scoring functions. This strategy harnesses the predictive power of machine learning and the physical constraints advantage of molecular docking. Evaluation experiments on multiple datasets demonstrate that, compared to using molecular docking or machine learning methods alone, our proposed strategy can significantly improve the success rate and accuracy of protein-ligand complex structure predictions.