Browse by Domain
Jump straight to the domain tracks in the README.
Canonical domains
These 6 canonical domain slugs (5 scientific domains + cross-domain) are the domain axis used to tag every entry on the site. Use cross-domain for general-purpose resources that don't target a single field.
drug-discovery-chemistry— cheminformatics, retrosynthesis, docking, chemistry agents.materials-science— interatomic potentials, materials APIs, discovery benchmarks.genomics-biology— protein and genomic foundation models, single-cell tooling, wet-lab automation.climate-earth-sciences— weather and climate models, earth observation, environmental data.physics-mathematics— symbolic regression, theorem proving, scientific ML toolchains.cross-domain— general-purpose entries (frameworks, surveys, foundational systems) that span fields.
See start-here for the full three-axis tagging convention.
Browse the cards
Domain
Drug discovery & chemistry
Cheminformatics, docking, retrosynthesis, chemistry agents, and platforms.
Tags: lifecycle:experiment-planning,tool-use-execution · domain:drug-discovery-chemistry · type:framework,agent-system,model
Domain
Materials science
Potentials, materials APIs, discovery benchmarks, and materials NLP.
Tags: lifecycle:experiment-planning,evaluation · domain:materials-science · type:framework,dataset,benchmark
Domain
Genomics & biology
Protein models, genomic foundation models, and single-cell tooling.
Tags: lifecycle:tool-use-execution,evaluation · domain:genomics-biology · type:model,framework
Domain
Climate & earth sciences
Weather models, climate foundation models, and evaluation resources.
Tags: lifecycle:experiment-planning,evaluation · domain:climate-earth-sciences · type:model,benchmark
Domain
Physics & mathematics
Symbolic regression, theorem proving, and scientific ML toolchains.
Tags: lifecycle:hypothesis-generation,evaluation · domain:physics-mathematics · type:framework,benchmark
Support
Cross-domain models
Use the model index when you want checkpoints that cut across domains.
Tags: lifecycle:tool-use-execution · domain:cross-domain · type:model
Resources by domain
Hand-picked entries grouped by canonical domain slug. Every bullet carries the full three-axis tag set (lifecycle: · domain: · type:) locked in Phase 2. Anchors below match the domain slugs verbatim so the card grid above and external links stay stable.
Drug discovery & chemistry
Cheminformatics, retrosynthesis, docking, and chemistry-aware agents. The distinctive shape here is chemistry-grounded tool use: models are wrapped with reaction templates, simulators, and safety checks so an agent can propose, score, and (sometimes) execute synthesis plans. Most live entries cluster around experiment-planning and tool-use-execution. Five to six flagships:
- RDKit — The de-facto open-source cheminformatics toolkit; substrate for almost every Python-based chemistry agent and ML pipeline. Tags:
lifecycle:tool-use-execution·domain:drug-discovery-chemistry·type:framework,tool - DeepChem — Open-source library for ML in chemistry, drug discovery, and materials, with a wide catalogue of featurizers, datasets, and pretrained models. Tags:
lifecycle:tool-use-execution,evaluation·domain:drug-discovery-chemistry·type:framework,dataset - AiZynthFinder — AstraZeneca's Monte Carlo Tree Search retrosynthesis tool with neural policies; canonical reference for synthesis planning. Tags:
lifecycle:experiment-planning·domain:drug-discovery-chemistry·type:framework,tool - ChemCrow — LLM agent equipped with 18 expert chemistry tools (synthesis, safety, search) for autonomous chemical reasoning. Tags:
lifecycle:tool-use-execution·domain:drug-discovery-chemistry·type:agent-system,framework - DiffDock — Diffusion-generative model for blind protein–ligand docking; a strong baseline for structure-based drug discovery agents. Tags:
lifecycle:tool-use-execution,evaluation·domain:drug-discovery-chemistry·type:model,paper - Coscientist — Boiko et al. autonomous agent (Nature 2023) that designs, plans, and executes chemistry experiments end-to-end; the canonical hypothesis-to-execution case study for the domain and the basis for its
domain:drug-discovery-chemistrytag in workflows. Tags:lifecycle:hypothesis-generation,tool-use-execution·domain:drug-discovery-chemistry·type:agent-system,paper
Materials science
Interatomic potentials, materials APIs, and discovery benchmarks. The frontier is ML force fields that are accurate enough to replace expensive DFT in screening loops, paired with active-learning campaigns that propose the next composition or structure to evaluate.
- Materials Project (mp-api) — Official Python client and REST surface for the Materials Project's computed-properties database covering hundreds of thousands of inorganic crystals; the standard data backbone for materials ML. Tags:
lifecycle:literature-intelligence,experiment-planning·domain:materials-science·type:dataset,tool - MACE — Equivariant message-passing interatomic potential; a leading open ML force field family for molecular dynamics and screening. Tags:
lifecycle:tool-use-execution·domain:materials-science·type:model,framework - Matbench — Standardised benchmark suite of materials property prediction tasks with leaderboards; the canonical evaluation surface for materials ML. Tags:
lifecycle:evaluation·domain:materials-science·type:benchmark,dataset - Open Catalyst Project — Meta + CMU dataset and challenge (OC20 / OC22 / OC25) for ML-driven catalyst discovery, with millions of DFT calculations. Tags:
lifecycle:experiment-planning,evaluation·domain:materials-science·type:dataset,benchmark - SciAgents — Multi-agent graph-reasoning framework for materials hypothesis generation across an ontological knowledge graph (Buehler). Tags:
lifecycle:hypothesis-generation·domain:materials-science·type:paper,agent-system
Genomics & biology
Protein and genomic foundation models, single-cell tooling, and wet-lab automation. AI-for-bio is unusual in that structure prediction works, which makes downstream design and screening pipelines tractable; the open challenge is closing the loop with real experiments.
- AlphaFold — DeepMind's protein structure prediction system; the canonical reference for AI-driven structural biology. Tags:
lifecycle:tool-use-execution·domain:genomics-biology·type:model,framework - ESM (Evolutionary Scale Modeling) — Meta's protein language models (ESM-2, ESMFold) for embeddings, structure, and function prediction at scale. Tags:
lifecycle:tool-use-execution·domain:genomics-biology·type:model - scvi-tools — Probabilistic deep generative models for single-cell omics (scRNA-seq, ATAC, spatial); the workhorse library of the scverse stack. Tags:
lifecycle:tool-use-execution,evaluation·domain:genomics-biology·type:framework,model - Boltz-1 — MIT's open-source biomolecular structure prediction model approaching AlphaFold 3 quality, released under MIT license. Tags:
lifecycle:tool-use-execution·domain:genomics-biology·type:model - LAB-Bench — FutureHouse benchmark for biology research tasks (literature, figures, protocols, DBQA, cloning); aimed squarely at scientific agents. Tags:
lifecycle:evaluation·domain:genomics-biology·type:benchmark,dataset
Climate & earth sciences
Weather and climate models, earth observation, and environmental data. The shift here has been from numerical PDE solvers to neural surrogates trained on reanalysis data — orders of magnitude faster, competitive in accuracy, and reshaping how forecasting and downscaling get done.
- GraphCast — DeepMind's GNN-based medium-range weather model that beats HRES on most variables at a fraction of the compute. Tags:
lifecycle:tool-use-execution,evaluation·domain:climate-earth-sciences·type:model,paper - Pangu-Weather — Huawei's 3D Earth-Specific Transformer for global weather forecasting; first ML model to outperform NWP at all forecast lead times in 2023. Tags:
lifecycle:tool-use-execution·domain:climate-earth-sciences·type:model,paper - Aurora — Microsoft's foundation model for atmospheric forecasting (weather, air quality, ocean waves) trained on heterogeneous earth-system data. Tags:
lifecycle:tool-use-execution·domain:climate-earth-sciences·type:model - WeatherBench 2 — Google Research benchmark and leaderboard for global weather forecasting at 6h–10d lead times; the standard evaluation surface for ML weather models. Tags:
lifecycle:evaluation·domain:climate-earth-sciences·type:benchmark,dataset - ClimateLearn — Open-source PyTorch library for ML-driven climate and weather modelling, with datasets, baselines, and downscaling tasks. Tags:
lifecycle:experiment-planning,evaluation·domain:climate-earth-sciences·type:framework,dataset
Physics & mathematics
Symbolic regression, theorem proving, and scientific ML toolchains. This is where AI is most directly producing machine-checkable artifacts — proofs, formulas, programs — rather than just predictions, which makes evaluation unusually clean.
- PySR — High-performance symbolic regression (Cranmer); recovers compact, interpretable equations from data and is widely used in physics ML. Tags:
lifecycle:hypothesis-generation,tool-use-execution·domain:physics-mathematics·type:framework,tool - Lean & mathlib — The Lean 4 theorem prover and its community-built mathematics library; the substrate for most modern AI-for-math systems. Tags:
lifecycle:tool-use-execution,evaluation·domain:physics-mathematics·type:framework,tool - DeepXDE — Library for physics-informed neural networks, operator learning, and inverse problems over PDEs and ODEs. Tags:
lifecycle:tool-use-execution·domain:physics-mathematics·type:framework - FunSearch — DeepMind system that pairs an LLM with an evaluator to search the space of programs and discover new mathematical results. Tags:
lifecycle:hypothesis-generation·domain:physics-mathematics·type:agent-system,paper - miniF2F — Olympiad-style formal mathematics benchmark across Lean, Metamath, and Isabelle; a standard evaluation surface for theorem-proving models. Tags:
lifecycle:evaluation·domain:physics-mathematics·type:benchmark,dataset