Codes for our paper "Programming Biomolecular Interactions with All-Atom Generative Model"
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Updated
Jun 6, 2026 - Python
Codes for our paper "Programming Biomolecular Interactions with All-Atom Generative Model"
pythonic interface to virtual screening software
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.
A comprehensive macromolecular library
Predicting protein-ligand binding sites using deep convolutional neural network
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
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
MD pharmacophores and virtual screening
A Euclidean diffusion model for structure-based drug design.
📐 Symmetry-corrected RMSD in Python
Open-source foundation of the user-sponsored PyMOL molecular visualization system.
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