Pafnucy -- A deep neural network for structure-based drug discovery

12/19/2017

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by Marta M. Stepniewska-Dziubinska, et al.

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Virtual screening 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 this process. 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. We have developed a new deep neural network tailored to 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 "scoring power" benchmark and Astex diverse set and outperformed classical scoring functions. The model, together with usage instructions and examples, is available as a git repository at http://gitlab.com/cheminfIBB/pafnucy

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Pafnucy -- A deep neural network for structure-based drug discovery

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

A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening

A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery

Detection of protein-ligand binding sites with 3D segmentation

Alpha Discovery Neural Network based on Prior Knowledge

Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference

Interpretable Machine Learning Models for the Digital Clock Drawing Test

Pafnucy -- A deep neural network for structure-based drug discovery

Related Research

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

A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening

A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery

Detection of protein-ligand binding sites with 3D segmentation

Alpha Discovery Neural Network based on Prior Knowledge

Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference

Interpretable Machine Learning Models for the Digital Clock Drawing Test