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elainejpai-14 / Protein-Ligand-Binding-Affinity-Prediction

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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.

machine-learning bioinformatics cheminformatics protein-data-bank rdkit transfer-learning uniprot bindingdb 3d-cnn protein-ligand-binding-affinity multimodal-learning graph-attention-networks graph-neural-networks pytorch-geometric molecular-graphs plip computational-drug-discovery rcsb-pdb pdbbind structural-deep-learning
  • Updated Jul 18, 2026
  • Jupyter Notebook

cgjuan01 / protein-ligand-binding-affinity-ageing-targets

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Leakage-aware ML pipeline predicting protein-ligand binding affinity for ageing-nominated targets: ChEMBL + BindingDB harmonisation, scaffold/cluster/temporal splits, explicit leakage audit, and conformal uncertainty.

benchmarking machine-learning cheminformatics drug-discovery rdkit uncertainty-quantification bindingdb model-evaluation chembl conformal-prediction data-leakage binding-affinity
  • Updated Jun 24, 2026
  • Python

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