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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

en970 / Equ-GNN

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E(3)-equivariant GNNs for protein-ligand binding-affinity prediction (EGNN + e3nn tensor-product), with a machine-precision invariance test suite.

bioinformatics deep-learning cheminformatics structural-biology protein-structure computational-biology pytorch drug-discovery message-passing geometric-deep-learning graph-neural-networks pytorch-geometric binding-affinity protein-ligand egnn e3nn equivariant-neural-networks se3-equivariance molecular-machine-learning pdbbind
  • Updated Jul 16, 2026
  • Python

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