Evaluating Molecular Property Prediction on QM9
Define QM9 targets and units, distinguish graph and geometry inputs, prevent evaluation leakage, and preserve original data provenance.
Guides
Learn core chemistry AI concepts, tool selection, deployment, and evaluation. Looking for concrete tool combinations and setup steps? Explore Recipes.
Molecular Graph Learning: PyG, e3nn, and DGL-LifeSci
Choose between graph-learning infrastructure, equivariant operations, and a chemistry-focused DGL toolkit while preserving evaluation and version boundaries.
Selecting ML Interatomic Potentials: MACE, SevenNet, CHGNet, and MatGL
Compare materials-oriented potential families and frameworks by training domain, outputs, calculator interfaces, asset rights, and reference validation.
Understanding Boltz-2: Structure Confidence and Small-Molecule Affinity
Read Boltz-2 confidence and affinity outputs without treating structural scores as binding measurements or predicted IC50 as Kd.
ASE with ML Potentials: Units, Optimization, and Validation
Understand ASE calculators, fixed and variable cells, force convergence, and the validation needed beyond a successful ML-potential relaxation.
Protein Design with AI: Backbone, Sequence, and Prediction
Distinguish RFdiffusion backbone generation, ProteinMPNN sequence design, and Boltz prediction, with explicit handoff and validation boundaries.
AI Agents for Chemistry: From Chatbots to Autonomous Research
A practical guide to eight chemistry and biomedical research agents: how tools, memory, planning and multi-agent roles work, which projects fit different tasks, and how to evaluate bounded autonomy with scientific oversight.
AI for Drug Discovery: Agents, MCP Servers and Platforms
Choose tools by workflow stage: target evidence, protein structures, known binding sites, molecular generation, property analysis and screening. Compare research agents, MCP integrations, hosted APIs and platforms, with a proposed end-to-end example and practical evaluation gates.
AI for Materials Science: Tools, Agents and Skills
A practical guide to materials databases, research agents and scientific Skills: how to organize crystal screening, computation and analysis while keeping convex-hull, equilibrium, phonon and experimental-stability claims distinct.
AI Skills for Chemistry: What They Are and How They Work
Understand chemistry AI Skills as reusable procedure modules: what SKILL.md contains, how hosts load instructions, how Skills differ from MCP and agents, and how to select, combine, and evaluate them without confusing guidance with scientific validation.
MCP vs Skills vs Agents vs APIs: What's the Difference?
Understand how APIs, MCP servers, Skills, and agents divide interface, procedural guidance, execution, and planning responsibilities in chemistry workflows—and how to choose or combine them without confusing access with scientific validity.
Protein & Structural Biology AI Workflow
Build an evidence-led protein workflow from UniProt identity and sequences to RCSB structure metadata, observed ligand contacts, and proposed downstream candidate evaluation—with explicit checks for species, isoforms, chains, missing data, and experimental validation.