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.
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)
drugdesign.org source of truth
Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.
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.
Two R shiny apps developed for analyzing differential scanning fluorimetry (DSF) data. One for binding, and one for stability
R shiny app to analyse microscale thermophoresis (MST) data
Performs large-scale ligand identification, curation and extraction from structures in the Protein Data Bank.
Computational workflows supporting Sobral et al., JCIM (2026). Includes AutoDock Vina docking setup, GROMACS 2024.4 MD parameter files, and Python analysis scripts for PSMA dual-target radioconjugates.
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.
Iambic Therapeutics — AI-driven drug discovery (oncology focus)
NCI-Profiler CLI — Non-covalent interaction analysis for protein-ligand complexes
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