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 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.
Geneformer
Transformer pretrained on about 30 million single-cell transcriptomes (Genecorpus-30M) that learns gene-network context from ranked expression, enabling few-shot prediction of gene dosage effects, cell states and candidate therapeutic targets.
TxGNN
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.
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.