FlexPose, a framework for AI-based flexible modeling of protein-ligand binding pose.
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Updated
Dec 27, 2023 - Python
FlexPose, a framework for AI-based flexible modeling of protein-ligand binding pose.
Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.
drugdesign.org source of truth
Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning
Code for "T Cell Receptor Specificity Prediction with Bimodal Attention Networks" (https://doi.org/10.1093/bioinformatics/btab294, ISMB 2021)
A Python tool for automated identification and topological analysis (QTAIM) of Critical Points at the interface between molecular fragments.
Identifies relevant ligands in the PDB
Open-sourced docking for small molecule to protein target. It prioritizes enhanced user-friendliness and accessibility.
High-throughput docking pose validation: symmetry-corrected RMSD and lightweight PoseBusters-style distance/clash filters.
Public repository of the PDBe ligand environment component
Template-based protein-ligand pose prediction with web interface.
R shiny app to analyse microscale thermophoresis (MST) data
Two R shiny apps developed for analyzing differential scanning fluorimetry (DSF) data. One for binding, and one for stability
A collection of GROMACS molecular dynamics simulation projects featuring protein-only and protein–ligand systems using CHARMM36, AMBER99SB, and physiological ionic conditions.
Agent skill for routing small-molecule design tasks to open tools, focused references, and license-aware implementation paths.
A modern web port of VHELIBS, the Validation Helper for LIgands and Binding Sites: analyse PDB/UniProt structures for RSR, RSCC, occupancy and R-free quality, browse results with interactive filters, and inspect ligands and their electron density in an in-browser Mol* 3D viewer.
Performs large-scale ligand identification, curation and extraction from structures in the Protein Data Bank.
Fine-tuning OpenFold3 (4.0.0) for PDE10A protein–ligand pose prediction via distribution-aware PDB-scale data augmentation: +0.20 PL LDDT, −2.3 Å ligand RMSD on held-out.
E(3)-equivariant GNNs for protein-ligand binding-affinity prediction (EGNN + e3nn tensor-product), with a machine-precision invariance test suite.
Real-time, physics-only protein-ligand force viewer in the browser — LJ+Coulomb force heatmap, SMILES to 3D ligands, PDB-by-ID, binding-site scan. No ML, no docking.
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