In recent years, the cheminformatics community has seen an increased success with machine learning-based scoring functions for estimating binding affinities and pose predictions. The prediction of protein-ligand binding affinities is crucial for drug discovery research. Many physics-based scoring functions have been developed over the years. Lately, machine learning approaches are proven to boost the performance of traditional scoring functions. In this study, a novel deep learning based scoring function (DLSCORE) was developed and trained on the refined PDBBind v.2016 dataset using 348 BINding ANAlyzer (BINANA) descriptors. The neural networks of the DLSCORE model have different number of fully connected hidden layers. Our model, an ensemble of 10 networks, yielded a Pearson R2 of 0.82, a Spearman Rho R2 of 0.90, Kendall Tau R2 of 0.74, an RMSE of 1.15 kcal=mol, and an MAE of 0.86 kcal=mol for our test set. This software is available on Github at https://github.com/sirimullalab/dlscore.git
DLSCORE: A Deep Learning Model for Predicting Protein-Ligand Binding Affinities
20 April 2018, Version 1
This content is a preprint and has not undergone peer review at the time of posting.
DLSCORE Supporting Information (1)