Flagship resources by domain

Sensible "if in doubt, start here" picks per scientific domain. These are the resources users most often want when they describe a task in plain language. Always confirm with the live catalog (fetch_catalog.py topic <slug>) before final recommendation — the catalog evolves and there may be something newer.

The catalog is the source of truth. Treat this file as a fast cheatsheet, not a directory.

Biology / Genomics

DNA foundation models - arcinstitute/evo2_40b — 40B-param DNA LM, 9.3T nucleotide pretraining; zero-shot variant effect prediction, sequence generation. Huge — use Inference Providers or a Space. - arcinstitute/evo2_7b — 7B instruction-tuned variant; runs on a single 24 GB GPU.

Protein language models - facebook/esm2_t33_650M_UR50D — 650M ESM2; standard for embeddings, structure prediction, mutation scoring. Strong default. - Smaller ESM2 variants (*_t30_150M_*, *_t12_35M_*) for laptop/CPU use.

Single-cell / transcriptomics - arcinstitute/Stack-Large (STACK) — single-cell foundation model with in-context learning across cell types. - Merck/TEDDY — 116M single-cell foundation models for genomics + drug discovery.

Antibodies - opig/OAS — Observed Antibody Space, ~1B antibody sequences. The standard antibody ML dataset.

Bioacoustics / ecology - EarthSpeciesProject/NatureLM-audio — first audio-LM for animal vocalizations. - EarthSpeciesProject/esp-aves2-sl-beats-all — self-supervised bioacoustic encoder.

Genomic corpora - arcinstitute/opengenome2 — curated prokaryotic + eukaryotic sequences for foundation-model pretraining.

Chemistry / Drug discovery

• SandboxAQ/AQAffinity — drug-target affinity prediction.
• SAIR dataset — 1M+ protein-ligand co-structures (see SandboxAQ blog post in catalog).
• For SMILES/molecular tasks: check the catalog chemistry topic for current best — molecular foundation models change frequently.

Materials science

• LeMaterial — large open materials database (see catalog blog post 2024-12-10).
• Crystal structure foundation models, perovskite datasets — fetch topic materials-science for the live list. Many ship pymatgen.Structure objects rather than tensors.

Physics

• PDE-solver datasets (topic physics --filter datasets) — magnetohydrodynamics, fluid dynamics, plasma.
• Physics-Informed Neural Networks (PINN) — methodology covered in catalog blog posts.

Climate / Earth science / Weather

• Weather-foundation-model entries under topic climate — these are often huge multi-modal models with structured atmospheric inputs (geopotential, temperature on multiple pressure levels).
• Satellite imagery / remote sensing models under topic earth-science.

Medicine / Pathology

• hugging-science/breast-cancer-detector-2 — image classification baseline from the org itself.
• Pathology + radiology foundation models — fetch topic medicine. Many are gated; check the model card for access requirements.

Mathematics / Scientific reasoning

• Kimina-Prover family — large theorem-proving models. Likely needs Inference Providers.
• topic scientific-reasoning for LLM-as-scientific-assistant work, paper QA, multi-step reasoning evals.

Astronomy

• Galaxy survey datasets (e.g., hugging-science/mmu_legacysurvey_dr10_south_21) — image-heavy, FITS format, may need astropy.
• Astronomical foundation models — fetch topic astronomy.

Cross-domain interactive demos (Spaces)

• hugging-science/boltzgen-demo — protein/peptide/nanobody binder design (the marquee demo).
• hugging-science/dataset-quest — discover and submit scientific datasets.
• hugging-science/science-release-heatmap — visualize who's publishing AI4Science resources.

How to use this list

1. User describes a task → match to a domain row above.
2. Fetch the live topic file with fetch_catalog.py topic <slug> and confirm the recommended resource still exists / is current.
3. Read the resource's HF card for input format, license, access requirements.
4. Follow using-datasets.md / using-models.md / using-spaces.md for the actual code.
5. If a related blog post is listed in the catalog, cite it when explaining methodology.

If nothing in this cheatsheet fits, run fetch_catalog.py search "<keyword>" against the full index. The catalog has hundreds of entries this file doesn't enumerate.