Predict protein-ligand and catalytic pockets and perform molecular docking of a specific ligand to each predicted pocket.
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Updated
Dec 15, 2022 - Python
Predict protein-ligand and catalytic pockets and perform molecular docking of a specific ligand to each predicted pocket.
Codes for our paper "Programming Biomolecular Interactions with All-Atom Generative Model"
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.
Official Github for "PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening" (Chemical Science)
pythonic interface to virtual screening software
A comprehensive macromolecular library
EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein
Predicting protein-ligand binding sites using deep convolutional neural network
Comprehensive library for fast, GPU accelerated molecular gridding for deep learning workflows
Identification of Protein-Ligand Binding Sites using dipolar EPR data
Library for computing dynamic non-covalent contact networks in proteins throughout MD Simulation
Open source code for AlphaFold 2.
Protein Ligand INteraction Dataset and Evaluation Resource
📐 Symmetry-corrected RMSD in Python
A Euclidean diffusion model for structure-based drug design.
MD pharmacophores and virtual screening
This package contains deep learning models and related scripts for RoseTTAFold
An open library to work with pharmacophores.
Experiments with expanded ensembles to explore chemical space
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