Predicting protein-ligand binding sites using deep convolutional neural network
Predict protein-ligand and catalytic pockets and perform molecular docking of a specific ligand to each predicted pocket.
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
A comprehensive macromolecular library
Comprehensive library for fast, GPU accelerated molecular gridding for deep learning workflows
Official Github for "PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening" (Chemical Science)
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.
Codes for our paper "Programming Biomolecular Interactions with All-Atom Generative Model"
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.
Interface for AutoDock, molecule parameterization
Identification of Protein-Ligand Binding Sites using dipolar EPR data
Library for computing dynamic non-covalent contact networks in proteins throughout MD Simulation
A Euclidean diffusion model for structure-based drug design.
📐 Symmetry-corrected RMSD in Python
Open source code for AlphaFold 2.
Protein Ligand INteraction Dataset and Evaluation Resource
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