ResAtom System: Protein and Ligand Affinity Prediction Model Based on Deep Learning

TL;DR

ResAtom is a deep learning-based model that predicts protein-ligand affinity accurately without relying on experimentally-determined conformations, enabling large-scale virtual screening in drug discovery.

Contribution

The paper introduces ResAtom, a novel deep learning model with attention mechanisms that achieves high correlation in affinity prediction without experimental conformations.

Findings

ResAtom-Score achieves R=0.833 on CASF-2016.

Combining ResAtom-Score with ΔVinaRF20 yields near-experimental accuracy.

ResAtom can be used for in silico screening of drug candidates.

Abstract

Motivation: Protein-ligand affinity prediction is an important part of structure-based drug design. It includes molecular docking and affinity prediction. Although molecular dynamics can predict affinity with high accuracy at present, it is not suitable for large-scale virtual screening. The existing affinity prediction and evaluation functions based on deep learning mostly rely on experimentally-determined conformations. Results: We build a predictive model of protein-ligand affinity through the ResNet neural network with added attention mechanism. The resulting ResAtom-Score model achieves Pearson's correlation coefficient R = 0.833 on the CASF-2016 benchmark test set. At the same time, we evaluated the performance of a variety of existing scoring functions in combination with ResAtom-Score in the absence of experimentally-determined conformations. The results show that the use of {\Delta}VinaRF20 in combination with ResAtom-Score can achieve affinity prediction close to scoring functions in the presence of experimentally-determined conformations. These results suggest that ResAtom system may be used for in silico screening of small molecule ligands with target proteins in the future. Availability: https://github.com/wyji001/ResAtom