18 skills found
zaixizhang / PocketGenPocketGen (Nature Machine Intelligence 24): Generating Full-Atom Ligand-Binding Protein Pockets
pulimeng / DeepDrug3DA convolutional neural network for classifying binding pockets based on spatial and chemical information extracted from the pockets.
jr-marchand / Caviar// PROJECT PAUSED FOR NOW (lack of capacity) // Protein cavity identification and automatic subpocket decomposition
Hoecker-Lab / PocketoptimizerBinding pocket optimization based on force fields and docking scoring functions
Weeks-UNC / FpocketRProgram to find drug-like RNA-ligand binding pockets.
durrantlab / SubpexSubPEx (Sub-Pocket Explorer) is a tool to enhance ensemble (multiple-receptor/relaxed-complex) virtual screening. It uses weighted ensemble path sampling and molecular dynamics simulations to accelerate binding-pocket sampling.
genki-kudo / Pocket To ConcavityPocket to Concavity is a tool for refinement of Protein-Ligand binding site shape from alpha-spheres
durrantlab / POVMEDetect and characterize binding pockets from molecular simulations.
accsc / GAsolGenetic algorithm to convert 3D-RISM solvent densities to explicit water molecules in binding pockets
BalytskyiJaroslaw / RAPID NetCode and demos corresponding to our paper:"Accurate Pocket Identification for Binding-Site-Agnostic Docking", https://arxiv.org/abs/2502.02371
kstep / Rust PocketPocket API bindings (http://getpocket.com), WIP
receptor-ai / Pocket CfdmAugmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets
chenyaoxi / DEPACT PACMatchThe DEPACT and PACMatch programs, which are used for de novo ligand-binding pocket design in the protein scaffold.
pascalbartschi / Geom Diffusion GuidanceStructure-based molecular diffusion with explicit geometric classifier guidance from protein side-chain interactions to direct ligand generation in binding pockets
HankerWu / Pocket ExtractionPocket Extraction is a Python package for extracting ligands and binding pockets from PDB files. It combines the power of Biopython and RDKit to provide flexible and efficient molecular structure processing.
ShipraMalhotra / PocketDruggabilityA model that predicts the “attainable binding affinity” for a given binding pocket on a protein; this model relies on 13 physiochemical and structural features calculated using the protein structure.
larngroup / TAG DTATAG-DTA: Binding Region-Guided Strategy to Predict Drug-Target Affinity Using Transformers
molinfo-vienna / Autopocket2crestAutoPocket2CREST — Automated pipeline for preparing protein–ligand binding pockets for CREST conformational sampling. This workflow extracts a local pocket around the bound ligand, adds hydrogens, formats the structure for CREST, generates backbone constraints, and runs the CREST conformer search—all in one reproducible process.
