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