ResAtom System: Protein and Ligand Affinity Prediction Model Based on Deep Learning
This addresses the problem of large-scale virtual screening in drug discovery by providing a method that does not rely on experimentally-determined conformations, though it appears incremental as it builds on existing deep learning approaches.
The researchers tackled protein-ligand affinity prediction for drug design by developing a deep learning model based on ResNet with attention, achieving a Pearson's correlation coefficient R = 0.833 on the CASF-2016 benchmark test set.
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 ΔVinaRF20 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