chem-materials

I need computational work for proteins, molecules, drugs, or materials

Organize structure prediction, molecular descriptors, docking, drug discovery, and materials tools into screening, modeling, interpretation, and reporting workflows.

Who it helps

Drug discovery and chemical biology researchersProtein design teamsComputational materials researchers

Bring these materials

Protein sequences, PDB, SMILES, SDF, or CIFScreening targets or property metricsCandidate molecule or material listsExisting experimental constraints

Skills used

cs.boltzcs.diffdocksa.rdkitsa.datamolsa.pymatgen

Before you start

Install the Skills for this workflow

Choose your system and agent, then run the command in your terminal. After installation, send the prompts below to your agent.

System
Agent
curl -sSL https://paperskills.com/scripts/paperskills-install.sh | bash -s -- \
  --tool codex \
  --skills cs.boltz,cs.diffdock,sa.rdkit,sa.datamol,sa.pymatgen \
  --registry https://paperskills.com/api/registry

Recommended workflow

Step 1 · sa.rdkit

Prepare structural or molecular inputs

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Task for your agent

Use rdkit/datamol to clean SMILES/SDF, generate descriptors, fingerprints, conformers, and similarity matrices, and flag invalid inputs.

Expected outputs

Standardized molecule tableDescriptor matrixInvalid input list

Step 2 · cs.diffdock

Predict structures or docking poses

View skill

Task for your agent

Use diffdock/boltz to predict binding poses or complex structures for candidate protein-ligand pairs, with confidence, failures, and validation suggestions.

Expected outputs

Structure predictionsConfidence scoresValidation suggestions

Step 3 · sa.pymatgen

Extend to material or property analysis

View skill

Task for your agent

If the target is a material structure, use pymatgen to read CIF/POSCAR, compute structural information, and plan phase diagram, band-structure, or Materials Project queries.

Expected outputs

Structure summaryMaterials query planProperty analysis path

boltz

Structure prediction for protein, nucleic-acid, and small-molecule complexes with Boltz-2 (Passaro & Wohlwend et al. 2025, github.com/jwohlwend/boltz). Reach for this skill to validate designed binders against a target, to co-fold a protein with a SMILES or CCD ligand, or to get an open-source AlphaFold3 alternative with optional binding-affinity prediction.

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diffdock

Predict small-molecule binding poses with DiffDock-L (Corso et al. 2023/2024, github.com/gcorso/DiffDock) — blind diffusion docking that places a ligand into a protein pocket without a predefined search box and ranks the samples with a learned confidence model. Reach for this skill to dock a SMILES or SDF against a PDB, to generate ranked 3D poses for a small fragment library, or to get a starting pose for downstream rescoring. DiffDock predicts geometry, not affinity.

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rdkit

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

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datamol

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

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pymatgen

Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.

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Lab automationFigures and presentations