Predicting protein-ligand binding sites using deep convolutional neural network
pythonic interface to virtual screening software
Codes for our paper "Programming Biomolecular Interactions with All-Atom Generative Model"
A comprehensive macromolecular library
Comprehensive library for fast, GPU accelerated molecular gridding for deep learning workflows
Predict protein-ligand and catalytic pockets and perform molecular docking of a specific ligand to each predicted pocket.
EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein
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.
Experiments with expanded ensembles to explore chemical space
A versatile workflow for the generation of receptor-based pharmacophore models for virtual screening
This package contains deep learning models and related scripts for RoseTTAFold
An open library to work with pharmacophores.
MD pharmacophores and virtual screening
📐 Symmetry-corrected RMSD in Python
A Euclidean diffusion model for structure-based drug design.
Library for computing dynamic non-covalent contact networks in proteins throughout MD Simulation
Identification of Protein-Ligand Binding Sites using dipolar EPR data
Interface for AutoDock, molecule parameterization
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