A versatile workflow for the generation of receptor-based pharmacophore models for virtual screening
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Updated
May 22, 2025 - Python
A versatile workflow for the generation of receptor-based pharmacophore models for virtual screening
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.
fpocket is a very fast open source protein pocket detection algorithm based on Voronoi tessellation. The platform is suited for the scientific community willing to develop new scoring functions and extract pocket descriptors on a large scale level. fpocket is distributed as free open source software.
Burrow-Wheeler Aligner for short-read alignment (see minimap2 for long-read alignment)
Official repository for the Boltz biomolecular interaction models
Code for the ProteinMPNN paper
A deep learning framework for molecular docking
Deep Site and Docking Pose (DSDP) is a blind docking strategy accelerated by GPUs, developed by Gao Group. For the site prediction part, several modifications are introduced to PUResNet program. The pose sampling part is similar as AutoDock Vina combined with a number of modifications.
This package contains deep learning models and related scripts for RoseTTAFold
Training and inference code for ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design [ICLR 2025 oral]
A Euclidean diffusion model for structure-based drug design.
TeachOpenCADD: a teaching platform for computer-aided drug design (CADD) using open source packages and data
Codes for our paper "Programming Biomolecular Interactions with All-Atom Generative Model"
Official Github for "PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening" (Chemical Science)
Semiempirical Extended Tight-Binding Program Package
Identification of Protein-Ligand Binding Sites using dipolar EPR data
[NeurIPS2025 Spotlight 🔥 ] Official implementation of "UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection"
Predict protein-ligand and catalytic pockets and perform molecular docking of a specific ligand to each predicted pocket.
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