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GNNExplainer explains predictions made by graph neural networks by learning sparse masks over subgraph structure and node features. Use for post-hoc GNN interpretability, identifying important edges a
Use with AI
Install the MCP server or CLI to instantly fetch GNNExplainer documentation:
Install command
claude mcp add biocontext7 -- npx @biocontext7/mcpOr share this page: biocontext7.com/tools/gnnexplainer
AntiFold — antibody-specific inverse folding model built on ESM-IF1, fine-tuned on antibody structures from SAbDab and OAS databases. Predicts residue log-likelihoods for antibody variable domains (IM
2 shared topics • 3 shared operations
Use when working with NetMHCpan 4.1 for MHC class I peptide binding and ligand presentation prediction. NetMHCpan accepts peptide lists or FASTA proteins, scores candidate ligands against up to 20 all
2 shared topics • 3 shared operations
Use when working with Sapiens — a BERT-based human antibody language model for antibody humanization, humanness scoring, and sequence design. Sapiens predicts humanizing mutations, scores residue-leve
2 shared topics • 3 shared operations
Geneformer — transformer-based foundation model pretrained on ~30 million single-cell transcriptomes for context-specific gene network analysis. Supports fine-tuning for cell type classification, gene
2 shared topics • 2 shared operations
ProteinMPNN — deep learning-based protein sequence design from backbone structures. Uses message passing neural networks to predict amino acid sequences that fold into a given 3D backbone. Supports fi
2 shared topics • 2 shared operations