TY - JOUR
AU1 - Stepniewska-Dziubinska, Marta M
AU2 - Zielenkiewicz, Piotr
AU3 - Siedlecki, Pawel
AB - MotivationStructure 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.ResultsWe 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 implementationThe model, together with usage instructions and examples, is available as a git repository at http://gitlab.com/cheminfIBB/pafnucy.Supplementary informationSupplementary data are available at Bioinformatics online.
TI - Development and evaluation of a deep learning model for protein–ligand binding affinity prediction
JF - Bioinformatics
DO - 10.1093/bioinformatics/bty374
DA - 2018-05-10
UR - https://www.deepdyve.com/lp/oxford-university-press/development-and-evaluation-of-a-deep-learning-model-for-protein-ligand-8dZa30argG
SP - 3666
EP - 3674
VL - 34
IS - 21
DP - DeepDyve
ER -