Protein-Ligand Interaction Profiler - Analyze and visualize non-covalent protein-ligand interactions in PDB files according to 📝 Schake, Bolz, et al. (2025), https://doi.org/10.1093/nar/gkaf361
LABODOCK: A Colab-Based Molecular Docking Tools
A web service and standalone Docker toolkit for the comparative analysis of protein-ligand interaction networks, powered by the PLIP engine. Published in In Silico Pharmacology.
Converts Boltz output CIF to standardized PDB and then runs PLIP to analyze protein-ligand interactions and produce Pymol session file.
This repository contains an end-to-end, fully GUI/web-based dual-receptor molecular docking and ADMET profiling pipeline of four cyanobacterial natural products — scytonemin, phycocyanobilin, lyngbyabellin A and nostocarboline — against the BRCA2 DNA-binding domain using PyMOL, AutoDock Vina, PLIP, SwissADME, and ADMETlab 3.0.
Config-driven docking pipeline: AutoDock, Vina, Smina in one workflow
Molecular analysis workstation — real protein-ligand contact analysis (PLIP), AutoDock Vina docking, GROMACS MD, and live data from RCSB, PubChem, ChEMBL, UniChem & BindingDB. Ships as a core26 snap.
Open-source in silico drug discovery pipeline targeting Mps1/TTK kinase — virtual screening of 45 known inhibitors + 210 novel candidates, PLIP interaction analysis, ADME filtering, QSAR (R²=0.73, 10-fold CV), and NTD allosteric target exploration. MSc Bioinformatics internship, Oxford Brookes.
Do structural priors help a co-folding model? PLIP constraints from crystals and DiffDock poses, fed to Boltz-2 and measured against an unconstrained baseline.
Prospective virtual screening of vendor catalogues against carbonic anhydrase with Boltz-2, plus DiffDock/PLIP constraint re-ranking
A comparative and ablation study exploring different models for protein-ligand binding affinity prediction. The model categories studied in this project include traditional machine learning, graph neural networks, and structural deep learning models.
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