AI in Oncology · AI Models
A researcher-grade catalog of AI models, datasets and open data needs in oncology — every card structured, sourced and dated.
Filter by task, data type, cancer, availability and regulatory status. Each card follows one model-card standard and links to Hugging Face, code, papers and the datasets it was trained or tested on. { } export JSON
AlphaFold 2 2
Predicts the 3D structure of a protein from its amino-acid sequence at near-experimental accuracy; the AlphaFold Protein Structure Database (with EMBL-EBI) provides predicted structures for over 200 million proteins, including cancer-relevant targets and mutants.
AlphaFold 3 3
Diffusion-based successor that predicts joint structures of proteins with DNA, RNA, ligands, ions and modified residues — the interaction types that matter for drug design and for understanding oncogenic complexes.
ESM-2 / ESMFold
Protein language models from 8 million to 15 billion parameters trained on UniRef sequences; embeddings power variant-effect and function prediction, and ESMFold predicts structure directly from a single sequence without MSAs.
Cards follow the cancer3.ai model-card standard: AI_MODEL_CARD_STANDARD.md. Corrections and new entries: contact the editorial team; every fact needs a public source.
This page is educational — it is not medical advice and does not replace consultation with an oncologist. Diagnostic and treatment decisions are made solely by specialist physicians.