ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
1/1
2 files

DLSCORE: A Deep Learning Model for Predicting Protein-Ligand Binding Affinities

preprint
submitted on 19.04.2018 and posted on 20.04.2018 by Md Mahmudulla Hassan, Daniel Castaneda Mogollon, Olac Fuentes, suman sirimulla

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


Funding

UTEP School of Pharmacy

History

Email Address of Submitting Author

ssirimulla@utep.edu

Email Address(es) for Other Author(s)

mhassan@miners.utep.edu, dcastaneda@miners.utep.edu, ofuentes@utep.edu

Institution

The University of Texas at El Paso

Country

USA

ORCID For Submitting Author

0000-0003-4665-6665

Declaration of Conflict of Interest

The authors did not declare any conflicts of interest.

Exports