Predicting protein-ligand binding sites using deep convolutional neural network
A comprehensive macromolecular library
pythonic interface to virtual screening software
EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein
Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.
Comprehensive library for fast, GPU accelerated molecular gridding for deep learning workflows
Codes for our paper "Programming Biomolecular Interactions with All-Atom Generative Model"
Predict protein-ligand and catalytic pockets and perform molecular docking of a specific ligand to each predicted pocket.
Official Github for "PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening" (Chemical Science)
Experiments with expanded ensembles to explore chemical space
A versatile workflow for the generation of receptor-based pharmacophore models for virtual screening
Open source code for AlphaFold 2.
Protein Ligand INteraction Dataset and Evaluation Resource
Identification of Protein-Ligand Binding Sites using dipolar EPR data
Library for computing dynamic non-covalent contact networks in proteins throughout MD Simulation
MD pharmacophores and virtual screening
An open library to work with pharmacophores.
This package contains deep learning models and related scripts for RoseTTAFold
Open-source foundation of the user-sponsored PyMOL molecular visualization system.
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