On Aug.11, Professor Tingjun Hou, Associate Professor Yu Kang and Researcher Peichen Pan published the newest research entitledABoosting Protein-Ligand Binding Pose Prediction and Virtual Screening Based on Residue-Atom Distance Likelihood Potential and Graph Transformer in Journal of Medicinal Chemistry.

The past few years have witnessed enormous progress toward applying machine learning approaches to the development of protein-ligand scoring functions. However, the robust performance and wide applicability of scoring functions remain a big challenge for increasing the success rate of docking-based virtual screening. Herein, a novel scoring function named RTMScore was developed by introducing a tailored residue-based graph representation strategy and several graph transformer layers for the learning of protein and ligand representations, followed by a mixture density network to obtain residue-atom distance likelihood potential. Our approach was resolutely validated on the CASF-2016 benchmark, and the results indicate that RTMScore can outperform almost all of the other state-of-the-art methods in terms of both the docking and screening powers. Further evaluation confirms the robustness of our approach that can not only retain its docking power on cross-docked poses but also achieve improved performance as a rescoring tool in larger-scale virtual screening.

This work was carried out by Postdoctor Chao Shen under the supervision of Professor Tingjun Hou, Associate Professor Yu Kang and Researcher Peichen Pan, CPS-ZJU.

Link: https://pubs.acs.org/doi/10.1021/acs.jmedchem.2c00991