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

Task Data type Cancer Clinical setting Availability Regulatory status Kind Search Sort × Clear filters

AlphaFold 3 3

Google DeepMind / Isomorphic Labs · 2024

Foundation model Gated weights (licence click-through) Not applicable

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.

Protein structure Drug discovery Protein sequence Small molecules Genomics (DNA)

Pan-cancer

GitHub Paper Open card →

TxGNN

Zitnik Lab, Harvard Medical School · 2024

Task-specific model Open weights Research use only

Graph neural network for zero-shot drug repurposing that scores drug–disease indications and contraindications over a medical knowledge graph of 17,080 diseases, including ones with no approved treatment.

Drug repurposing Drug discovery Small molecules Biomedical literature

Pan-cancer

GitHub Paper Open card →

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.