Development and evaluation of a deep learning model for protein–ligand binding affinity prediction

By Marta M. Stepniewska-Dziubinska, Piotr Zielenkiewicz, Pawel Siedlecki. Bioinform., 2017

530 citations recorded by the source corpus.

Motivation: Structure based ligand discovery is one of the most successful approaches for augmenting the drug discovery process. Currently, there is a notable shift towards machine learning (ML) methodologies to aid such procedures. Deep learning has recently gained considerable attention as it allows the model to ‘learn’ to extract features that are relevant for the task at hand. Results: We have developed a novel deep neural network estimating the binding affinity of ligand‐receptor complexes. The complex is represented with a 3D grid, and the model utilizes a 3D convolution to produce a feature map of this representation, treating the atoms of both proteins and ligands in the same manner. Our network was tested on the CASF‐2013 ‘scoring power’ benchmark and Astex Diverse Set and outperformed classical scoring functions. Availability and implementation: The model, together with usage instructions and examples, is available as a git repository at http://gitlab.com/cheminfIBB/pafnucy. Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9114a51ef81d88f256e574df1ac8f1a59416f54f

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Citations in the corpus

A geometric deep learning approach to predict binding conformations of bioactive molecules (2021, background): “The latter evaluates whether the scoring function can identify true binders for a particular target using the EF (equation (19)) as the metric:”
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity (2021, methodology): “To comprehensively evaluate the model performance, following [35, 46], we use Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson’s correlation coefficient (R) and the standard deviation (SD) in regression to measure the prediction error.”
AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening (2019, unknown): “Note that, K DEEP , Pafnucy, PLEC-nn, and EIC-Score all carry out the predictions on the original PDBbind v2016 core set with N = 290.”