Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors

22 December 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 pK units and a Pearson’s correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks. We therefore show that the model can be combined with the classical scoring function AutoDock Vina in the context of ∆-learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina, ∆-AEScore has an RMSE of 1.32 pK units and a Pearson’s correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the good docking and screening power of the underlying classical scoring function.


feed-forward network
neural network
virtual screening

Supplementary materials


Supplementary weblinks


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