Therefore, we show that the model can be combined with the classical scoring function AutoDock Vina in the context of \(\Delta\)-learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. However, AEScore does not perform as well in docking and virtual screening tasks, for which it has not been explicitly trained. 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 p K units and a Pearson’s correlation coefficient of 0.83 for the CASF-2016 benchmark. 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. 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.
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