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

• URL: http://arxiv.org/abs/2105.05125v1
• Date: Sat, 17 Apr 2021 15:37:10 GMT
• Title: ResAtom System: Protein and Ligand Affinity Prediction Model Based on Deep Learning
• Authors: Yeji Wang, Shuo Wu, Yanwen Duan, Yong Huang
• Abstract summary: We build a predictive model of protein-ligand affinity through the ResNet neural network with added attention mechanism. The results show that the use of DeltaVinaRF20 in combination with ResAtom-Score can achieve affinity prediction close to scoring functions.
• Score: 1.1493209685387984
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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