Chem Brainstorm

Chem-Brainstorm — Computational Chemistry Thinking Guide

A flexible brainstorming framework for comp chem problems. Not a rigid checklist — activate only the steps relevant to the problem at hand. For complex/recurring workflows, load the appropriate references/protocol-*.md.

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Step 1 — Classify the Problem

Identify which mode(s) apply (can be multiple):

| Mode | Keywords | Protocol | |------|----------|---------| | MOL | "evaluate this molecule", "properties of X", "is this drug-like?" | references/protocol-mol-evaluation.md | | HYP | "why does X work?", "what target?", "generate hypotheses", "SAR" | references/protocol-hypothesis.md | | RXN | "will this reaction work?", "retrosynthesis", "conditions", "mechanism" | references/protocol-reaction.md | | PIPE | "screen a library", "build a workflow", "automate", "batch" | references/protocol-pipeline.md |

For simple one-off questions (e.g. "LogP of aspirin"), skip to Step 3 directly.

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Step 2 — Audit Inputs

What is available?

| Input type | Examples | |-----------|---------| | Structure | SMILES, SDF, MOL, InChI, name | | Library | .smi, .sdf, CSV with SMILES column | | Target | Gene name, UniProt ID, PDB ID | | Reaction | Reaction SMILES, SMARTS, conditions | | Data | IC50/EC50 table, experimental results | | None | Starting from a concept only |

Missing inputs → note what to fetch (Step 3 MCPs).

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Step 3 — Tool Map

Match inputs and goal to available tools. Order = cost (⚡ fast → ⚡⚡⚡ expensive).

ALKYL Scripts (local, instant)

| Goal | Script | Cost | |------|--------|------| | Properties, Lipinski, PAINS | chem_props.py | ⚡ | | Drug-likeness filters (Ro5/Veber/Egan/PAINS) | chem_filter.py | ⚡ | | Full structural analysis (FG, stereo, QED, SA) | chem_analyze.py | ⚡ | | Protonation state at pH | chem_pka.py --ph 7.4 | ⚡ | | CYP450 metabolic soft spots | chem_metabolism.py | ⚡ | | Scaffold, BRICS fragments | chem_scaffold.py | ⚡ | | Tautomers, stereoisomers | chem_tautomers.py / chem_enum.py | ⚡ | | Substructure / similarity search | chem_search.py | ⚡ | | Compare two molecules (MCS, Δprop) | chem_compare.py | ⚡ | | Apply reaction SMARTS | chem_react.py | ⚡ | | Batch process a library | chem_batch.py | ⚡ | | Diverse subset selection | chem_diversity.py | ⚡ | | 3D conformer generation | chem_3d.py | ⚡⚡ | | QM input (ORCA/Gaussian) | chem_qm.py | ⚡⚡⚡ |

ALKYL Skills (conceptual / coding help)

| Goal | Skill | |------|-------| | SMILES / SMARTS / SMIRKS writing | daylight-theory | | RDKit code (any cheminformatics) | rdkit | | ML on molecules (GCN, QSAR, MoleculeNet) | deepchem | | Retrosynthesis, generative ML | torchdrug | | Reaction graph analysis, ITS/DPO | synkit | | HPC/cloud pipeline | nextflow |

MCPs (external data, use when local data insufficient)

| Goal | MCP | Key functions | |------|-----|--------------| | Bioactivity, known targets, similar drugs | ChEMBL | compound_search, get_bioactivity, target_search, get_mechanism | | Target-disease associations, tractability | OpenTargets | search_entities, query_open_targets_graphql | | Recent methods, preprints, benchmarks | bioRxiv | search_preprints, get_preprint | | Clinical context, indications, endpoints | ClinicalTrials | search_trials, analyze_endpoints |

Marketplace Skills (specialized)

| Goal | Skill | |------|-------| | ADME/tox datasets, ML oracles | scientific-skills:pytdc | | Protein structure (AlphaFold, PDB) | scientific-skills:pdb-database | | Purchasable compounds | scientific-skills:zinc-database | | Approved drugs + interactions | scientific-skills:drugbank-database | | DiffDock virtual screening | scientific-skills:diffdock | | Cloud QM (DFT, pKa, Boltz) | scientific-skills:rowan |

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Step 4 — Propose Directions

Generate 2–3 concrete directions, each annotated with:

• Cost estimate (⚡/⚡⚡/⚡⚡⚡)
• First concrete action (script call or MCP query)
• What the result will tell us

Format:

Direction 1 — [name] ⚡
→ Action: python chem_filter.py --smiles "..."
→ Tells us: drug-likeness baseline before investing further

Direction 2 — [name] ⚡⚡
→ Action: ChEMBL compound_search + get_bioactivity
→ Tells us: known activity landscape for this scaffold

Direction 3 — [name] ⚡⚡⚡
→ Action: chem_3d.py → diffdock against PDB:XXXX
→ Tells us: predicted binding pose and affinity

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Step 5 — Sanity Checks (auto-activate if SMILES available)

Always run before investing in expensive steps:

chem_filter.py  → Lipinski, Veber, PAINS alerts
chem_analyze.py → SA score (> 6 = hard to synthesize), QED
chem_pka.py     → dominant form at physiological pH 7.4
chem_metabolism.py → CYP450 liabilities

Red flags that change the plan:

• SA score > 6 → consider simpler analog or retrosynthesis first
• PAINS alert → flag as assay interference risk
• net_charge ≠ 0 at pH 7.4 → affects permeability/docking

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Step 6 — Literature Anchor (activate for HYP mode or novel targets)

bioRxiv.search_preprints(query="[target/method]", date_range="2024-2026")
→ find recent methods, negative results, benchmark conditions

ChEMBL.target_search(query="[gene]")
→ confirm target is druggable, find reference ligands

OpenTargets.search_entities(query="[target]")
→ genetic evidence, disease associations, tractability score

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Decision Tree

Got SMILES?
  └─ Yes → run sanity checks (Step 5) first
  └─ No  → fetch from ChEMBL/PubChem or start from concept

Simple property question?
  └─ Yes → Step 3 scripts directly, no need for full workflow

Need external data?
  └─ Yes → MCPs before running expensive local tools

Complex / reproducible workflow?
  └─ Yes → load references/protocol-*.md

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Related Skills

• daylight-theory — write correct SMARTS/SMIRKS for queries and transforms
• rdkit — implement any cheminformatics step in Python
• synkit — if the problem involves reaction mechanisms or networks

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Kdevos12
• Source: Kdevos12/ALKYL
• License: MIT

Install and usage instructions live in the source repository linked above.