Rowan Autosearch

Agent-driven molecular property optimization. You define a chemistry objective and a starting molecule. An AI coding agent (Claude Code, Cursor, Codex, OpenCode, Gemini, etc.) plays the role of a medicinal chemist: proposing analogs, scoring them with Rowan quantum/ML workflows, checking drug-likeness with RDKit, and iterating until a winning candidate emerges.

rowan-autosearch is the scoring harness and lab notebook the agent uses to do this rigorously: every candidate, every Rowan payload, every constraint check, and every design rationale is captured as append-only JSON and rendered into an auditable HTML report.

Joint work between K-Dense and Rowan Scientific.

Recommended driver models: Claude Opus 4.8, GPT 5.5, or Gemini 3.1 Pro. Any agent that can read AGENTS.md and call CLI tools will work.

Stay up to date: Follow K-Dense on X, LinkedIn, and YouTube for release announcements, molecular optimization walkthroughs, and new Rowan workflow examples you can run with your own AI agent.

Who This Is For

Computational chemists running iterative property optimization (logS, pKa, redox potential, logP/logD, BDE, descriptors, docking) without writing custom enumeration code.
Medicinal chemists who want a chemistry-aware AI partner to brainstorm analogs, score them with reproducible quantum/ML methods, and keep an auditable trail.
Computational biologists doing structure-based ligand design who want to drive Rowan batch docking against a defined pocket and track drug-likeness alongside docking score.
Method developers who want a reproducible benchmark harness for new Rowan workflows or composite objectives.

If you can describe a molecule and a measurable goal in one sentence ("maximize logS while keeping MW < 250 and preserving the carboxylic acid"), you can drive a run.

What You Get

Each run produces:

A versioned config.json pinning the objective, starting SMILES, Rowan workflow, metric extractor, constraints, and (optionally) a weighted composite objective.
An append-only iteration log under iterations/NNNN.json capturing, per candidate: SMILES, parent SMILES, the agent's design note, RDKit descriptors (MW, logP, TPSA, HBD/HBA, rotatable bonds, heavy atoms, rings), constraint pass/fail, the extracted Rowan metric, the full raw Rowan object_data (for re-extraction or audit), and the workflow UUID.
An HTML report (report.html) with: progress and best-so-far curves, a Pareto plot (score vs. MW), a 2D structure grid of top candidates, a parent-to-child genealogy diagram, candidate tables, per-iteration rationales, and a 3D view of the current winner.

Everything in runs/<run_id>/ is plain JSON + PNG + HTML — easy to commit, diff, share, or post-process.

How The Loop Works

The agent is the optimizer. The CLI handles bookkeeping, parallel Rowan submissions, metric extraction, constraint checking, and reporting. There is no built-in Bayesian optimizer or genetic algorithm — that is intentional, so the agent's chemical reasoning stays in the loop and on the record.

See AGENTS.md for the full driver protocol an agent follows, and docs/agent-decision-loop.png for a visual summary of the agent's continue / converged / stuck decision cycle.

Features

Rowan workflows supported

rowan-score calls rowan.submit_<workflow_type>_workflow(...) from the Rowan v3 SDK. SMILES-first workflows that work out of the box:

Workflow	Typical use
`solubility`	Aqueous / organic solubility (`logS`, `logS_25C`)
`pka` / `macropka`	Acid/base ionization tuning
`redox_potential`	Oxidation / reduction potential (batteries, dyes, redox catalysts)
`admet`	`logP`, `logD`, predicted solubility
`descriptors`	Cheap descriptor sweeps (`logP`, `tpsa`)
`bde`	Bond dissociation energies
`electronic_properties`, `fukui`	Frontier orbital, reactivity descriptors
`hydrogen_bond_basicity`, `hydrogen_bond_donor_acceptor_strength`	H-bond strength
`conformer_search`, `tautomer_search`, `solvent_dependent_conformers`	Conformational sampling
`nmr`	Predicted NMR shifts
`ion_mobility`, `membrane_permeability`	Transport-relevant properties
`multistage_optimization`, `scan`, `irc`, `strain`	Geometry / reaction-path workflows
`docking`, `batch_docking`	Structure-based scoring against a protein pocket

Workflows that need extra non-SMILES inputs (proteins, pockets, ligand sets, spin states, reaction endpoints) are reachable by passing those through --workflow-param — see Run configuration.

Workflow parameter passthrough

Any Rowan v3 SDK argument is exposed via repeatable --workflow-param key=json_value:

--workflow-param method=kingfisher
--workflow-param solvents='["water","ethanol"]'
--workflow-param temperatures='[298.15]'
--workflow-param protein=<rowan-protein-uuid>
--workflow-param pocket='[[0,0,0],[12,12,12]]'
--workflow-param exhaustiveness=8

Values parse as JSON when possible (lists, numbers, booleans), falling back to strings. This is how you tune solubility methods, pKa methods, descriptor solvents, docking pockets, exhaustiveness, etc., without editing Python.

Metric extraction

For common workflows, rowan-score knows where the scalar lives in Rowan's object_data payload (e.g. solubilities.O.solubilities.0 for aqueous logS). For anything else, pass --metric-path <dot.path>, or score the baseline once and pin the path after inspecting the saved object_data.

Composite (weighted) objectives

Mix Rowan-derived numbers and local RDKit descriptors into one scalar score. Each term has a goal (maximize, minimize, or target with a numeric target) and a weight:

--objective-term '{"name":"logS","path":"solubilities.O.solubilities.0","goal":"maximize","weight":1.0}' \
--objective-term '{"name":"mw","source":"local","metric":"mw","goal":"minimize","weight":0.01}'

Local metric sources: mw, logp_crippen, tpsa, hbd, hba, rotb, heavy_atoms, rings. Per-term contributions are saved alongside the combined score for full transparency.

Local RDKit constraints (drug-likeness gates)

Constraints are checked before spending Rowan credits. A failing candidate is still scored and logged for visibility, but it does not count toward best-so-far.

--constraint mw_max=350      # molecular weight maximum (Da)
--constraint mw_min=150      # molecular weight minimum
--constraint logp_max=4      # Crippen logP maximum
--constraint logp_min=-1     # Crippen logP minimum
--constraint tpsa_max=140    # topological polar surface area max (Å²)
--constraint tpsa_min=20     # topological polar surface area min
--constraint hbd_max=5       # H-bond donors maximum
--constraint hba_max=10      # H-bond acceptors maximum
--constraint rotb_max=10     # rotatable bonds maximum
--constraint ha_max=40       # heavy atoms maximum
--constraint ha_min=8        # heavy atoms minimum
--constraint rings_max=5     # ring count maximum

These cover Lipinski/Veber-style filters; Rowan-output constraints (e.g. "pKa must be 4–6") are handled by including them as composite objective terms.

Parallel scoring + batch docking

Non-batch workflows are submitted concurrently with a ThreadPoolExecutor (--max-workers <n>). For batch_docking, the entire candidate list is submitted as a single Rowan batch workflow, which is the right choice for scoring large ligand sets against a fixed pocket.

Brainstorming helper (`rowan-propose`)

When the agent runs out of ideas, RDKit-based mutation strategies generate new SMILES variants:

bioisostere — common medchem swaps (COOH↔tetrazole, COOH↔sulfonamide, OH↔NH₂, benzene↔pyridine/pyrimidine, methyl↔F/CF₃, …).
scan-subst — aromatic H replacement with F, Cl, Br, Me, OMe, CF₃, CN, NO₂, NH₂.
brics — RDKit BRICS fragment decomposition + recombination.
all — all of the above.

Output is pre-formatted as SMILES|PARENT|NOTE; the agent should still filter for chemical sense before scoring.

Surrogate active-learning advisor (`rowan-suggest`)

A quantitative co-pilot that keeps the agent as the optimizer but stops it from spending Rowan credits blind. It learns a cheap surrogate (Morgan-fingerprint random forest by default, or a Tanimoto-kernel Gaussian process) from the run's own scored history and uses it to:

dedup candidate SMILES against molecules already evaluated (canonical SMILES match), so credits are never re-spent;
rank proposals by Expected Improvement over the current best, direction-aware, with a predicted value and uncertainty for each;
annotate novelty (Tanimoto distance to the evaluated set) and Bemis-Murcko scaffold, and filter out candidates that fail the run's RDKit constraints;
select a chemically diverse top-k via MaxMin so the search does not collapse into one chemotype;
report its own trustworthiness via cross-validated R^2/MAE, and fall back to pure novelty ranking (clearly labeled) when there is too little data to model.

It is entirely local — no Rowan calls — and emits paste-ready SMILES|PARENT|NOTE lines for rowan-score (or structured --json). See docs/active-learning.md.

# Rank the agent's own proposals before scoring:
uv run rowan-suggest --run <run_id> \
  --candidate "<SMILES>|<PARENT>|<NOTE>" \
  --candidate "<SMILES>|<PARENT>|<NOTE>" \
  --top-k 4

# Or generate and rank in one shot from a parent:
uv run rowan-suggest --run <run_id> --from-parent "<parent>" \
  --strategy all --n 20 --top-k 4

Auditable HTML report

rowan-report regenerates plots and report.html from scratch on demand:

progress.png — score per iteration, with constraint-aware best-so-far trace.
pareto.png — primary metric vs. MW (drug-likeness trade-off view).
grid.png — top-K 2D structures.
genealogy.png — parent-to-child edges across iterations, color-coded by improvement.
chemical_space.png — 2D PCA of Morgan fingerprints, colored by score, for chemotype-coverage at a glance.
parity.png — the surrogate's cross-validated predicted-vs-actual accuracy (how much to trust rowan-suggest).
report.html — embedded summary, plots, sortable candidate tables, per-iteration rationales, and a 3D view of the current winner.

Append-only audit trail

The full Rowan response is saved verbatim per candidate. If you discover later that you wanted a different metric path (say, free energy of solvation in DMSO instead of water), you can re-extract from the existing JSONs without re-running Rowan.

Setup

Requirements: Python 3.12+, a Rowan API key from labs.rowansci.com/account/api-keys.

# Recommended (uv):
uv venv
uv pip install -e .

# Or pip:
pip install -e .

# Configure credentials:
cp .env.example .env
# Edit .env and set ROWAN_API_KEY=...

Console scripts installed:

rowan-state    initialize / inspect runs
rowan-score    score candidates with Rowan + RDKit
rowan-report   regenerate plots and report.html
rowan-propose  RDKit-based brainstorming helper
rowan-suggest  surrogate active-learning advisor (dedup + rank by EI)

5-Minute Quickstart: aqueous solubility from aspirin

1. Initialize the run.

uv run rowan-state init \
  --run aspirin_solubility \
  --objective "Maximize aqueous solubility (logS) starting from aspirin while keeping MW < 250 and preserving the carboxylic acid" \
  --direction maximize \
  --metric logS \
  --workflow solubility \
  --start-smiles "CC(=O)Oc1ccccc1C(=O)O" \
  --workflow-param method=kingfisher \
  --workflow-param solvents='["water"]' \
  --workflow-param temperatures='[298.15]' \
  --constraint mw_max=250 \
  --candidates-per-iter 4 \
  --max-iter 12

2. Hand off to your coding agent. Open this repo in Claude Code, Cursor, Codex, OpenCode, or another agentic coding tool, and copy-paste the prompt below into the agent chat verbatim:

Drive run `aspirin_solubility` per AGENTS.md. Score one baseline candidate
first to confirm metric_path, then proceed with the loop until converged
or stuck.

That's the entire handoff. The agent reads AGENTS.md, scores the starting molecule to verify metric extraction, then proposes and scores variants iteratively.

3. Inspect progress at any time:

open runs/aspirin_solubility/report.html in your browser

For more worked examples (pKa, redox, logD, docking, composite objectives), see examples/.

Use Cases & Recipes

Each line below is a complete --workflow + --metric + --direction recipe that works against the built-in metric extractors.

Goal	Workflow & metric
Improve aqueous solubility	`--workflow solubility --metric logS --direction maximize`
Lower a phenol/acid pKa (more acidic)	`--workflow pka --metric pka --direction minimize`
Raise an amine pKa (more basic)	`--workflow pka --metric pka --direction maximize`
Tune redox potential (battery / dye / catalysis)	`--workflow redox_potential --metric redox_potential --direction maximize` (or `minimize`)
Lower lipophilicity	`--workflow descriptors --metric logP --direction minimize`
Hit a TPSA target	`--workflow descriptors --metric tpsa --direction maximize` (or `minimize`)
ADMET balancing	`--workflow admet --metric logD --direction minimize`
Find weakest bond (mech/photostability)	`--workflow bde --metric bde --direction minimize`
Structure-based docking	`--workflow batch_docking --metric docking_score --direction minimize` with `--workflow-param protein=<uuid> --workflow-param pocket='[[x1,y1,z1],[x2,y2,z2]]'`

Worked examples ready to paste:

examples/aspirin_solubility.md — maximize logS.
examples/phenol_pka.md — lower pKa with EWGs.
examples/quinone_redox.md — tune reduction potential.
examples/anthraquinone_battery_redox.md — organic battery scaffold tuning.
examples/ibuprofen_logp.md — lower logP, preserve pharmacophore.
examples/lidocaine_logd.md — lower logD for local anesthetics.
examples/caffeine_tpsa.md — push TPSA up (lower CNS penetration).
examples/coumarin_dye_solubility.md — water-solubilize a dye.
examples/aspirin_balanced_objective.md — weighted logS + MW objective.

Command Reference

`rowan-state init` — start a run

uv run rowan-state init \
  --run <run_id> \
  --objective "<plain-English goal>" \
  --direction maximize|minimize \
  --metric <metric_name> \
  --workflow <workflow_type> \
  --start-smiles "<SMILES>" \
  [--metric-path <dot.path>] \
  [--workflow-param key=json_value ...] \
  [--constraint key=value ...] \
  [--objective-term '<json>' ...] \
  [--candidates-per-iter <n>] \
  [--max-iter <n>] \
  [--force]

Option	Notes
`--run`	Slug used for `runs/<run_id>/`.
`--objective`	Plain-English; the agent reads this when proposing.
`--direction`	`maximize` (larger metric is better) or `minimize`.
`--metric`	Metric name. Use a built-in alias (see below) or any custom name with `--metric-path`.
`--workflow`	Rowan workflow suffix. `rowan-score` calls `submit_<workflow>_workflow`.
`--start-smiles`	Root molecule for the search.
`--metric-path`	Dot-path into Rowan `object_data` (e.g. `solubilities.O.solubilities.0`). Optional when the built-in extractor knows your workflow + metric.
`--workflow-param`	Repeatable. Passed straight to the Rowan SDK submitter.
`--constraint`	Repeatable. RDKit-based drug-likeness gates.
`--objective-term`	Repeatable. JSON term for a weighted composite score.
`--candidates-per-iter`	Target batch size for the agent (default 4).
`--max-iter`	Stopping budget (default 50).
`--force`	Overwrite an existing config.

Built-in metric aliases (no --metric-path needed):

Workflow	Metric aliases
`solubility`	`logS`, `logS_25C`
`pka`	`pka`
`redox_potential`	`redox_potential`
`admet`	`logP`, `logD`, `solubility`
`bde`	`bde`
`descriptors`	`logP`, `tpsa`
`docking`, `batch_docking`	`docking_score`

`rowan-state status` / `rowan-state list`

uv run rowan-state status --run <run_id>   # objective, iter count, best-so-far
uv run rowan-state list                    # one line per run

`rowan-score` — score candidates

uv run rowan-score --run <run_id> \
  --rationale "Lab-notebook paragraph for this iteration." \
  --candidate "<SMILES>|<PARENT_SMILES>|<DESIGN_NOTE>" \
  --candidate "<SMILES>|<PARENT_SMILES>|<DESIGN_NOTE>" \
  [--decision continue|converged|stuck] \
  [--max-workers <n>] \
  [--dry-run]

For each candidate, rowan-score:

Parses SMILES with RDKit.
Computes local descriptors (MW, logP, TPSA, HBD, HBA, rotatable bonds, heavy atoms, rings).
Checks local constraints.
Submits the configured Rowan workflow (in parallel, or as one batch for batch_docking).
Saves the full Rowan object_data.
Extracts the primary metric — or, if a composite objective is configured, computes the weighted score and saves per-term contributions.
Appends a new iterations/NNNN.json.

PARENT_SMILES may be empty (SMILES||note) for the baseline.

--dry-run validates formatting without calling Rowan.

When no --candidate is supplied, rowan-score does not call Rowan. Use --decision converged|stuck to update the latest iteration's decision, or omit --decision to append a note-only wrap-up iteration that inherits the latest decision.

`rowan-report` — rebuild plots + HTML

uv run rowan-report --run <run_id> [--top-k 8]

Regenerates plots/*.png and report.html from the iteration history. Safe to run any time.

`rowan-propose` — brainstorming helper

uv run rowan-propose --smiles "<parent>" --strategy bioisostere|scan-subst|brics|all --n 8

Output is SMILES|PARENT|NOTE lines, ready to feed into rowan-score. Treat as inspiration, not auto-submit.

`rowan-suggest` — surrogate active-learning advisor

uv run rowan-suggest --run <run_id> \
  [--candidate "<SMILES>|<PARENT>|<NOTE>" ...] \
  [--from-parent "<parent>" --strategy bioisostere|scan-subst|brics|all --n 20] \
  [--top-k 4] [--xi 0.01] [--model rf|gp] \
  [--diverse|--no-diverse] \
  [--respect-constraints|--no-respect-constraints] \
  [--include-seen] [--json]

Learns from the run's scored history, dedups against already-evaluated molecules, ranks by Expected Improvement (or novelty in cold start), and recommends a diverse, constraint-passing subset as paste-ready SMILES|PARENT|NOTE lines.

Option	Notes
`--candidate`	Repeatable. Proposals to screen/rank.
`--from-parent`	Auto-generate variants via `rowan-propose` from this parent.
`--strategy` / `--n`	Strategy and per-strategy count for `--from-parent`.
`--top-k`	How many candidates to recommend (default 4).
`--xi`	Expected-improvement exploration parameter (default 0.01).
`--model`	`rf` (random forest, default) or `gp` (Tanimoto-kernel GP).
`--diverse`	Apply MaxMin diversity over the top pool (default on).
`--respect-constraints`	Drop candidates that fail the run's RDKit gates (default on).
`--include-seen`	Include molecules already evaluated in this run.
`--json`	Emit structured JSON instead of paste-ready lines.

Run Outputs

runs/<run_id>/
├── config.json                 # the run contract
├── iterations/
│   ├── 0001.json               # baseline / first scoring batch
│   ├── 0002.json
│   └── ...                     # append-only
├── plots/
│   ├── progress.png            # score per iter + best-so-far
│   ├── pareto.png              # score vs MW
│   ├── grid.png                # top-K 2D structures
│   ├── genealogy.png           # parent → child edges
│   ├── chemical_space.png      # PCA of fingerprints, colored by score
│   └── parity.png              # surrogate cross-validated accuracy
└── report.html                 # everything stitched together + 3D winner

A candidate record (abridged):

{
  "smiles": "O=C(O)c1ccccc1O",
  "parent_smiles": "CC(=O)Oc1ccccc1C(=O)O",
  "design_note": "deacetylate to phenolic salicylate",
  "metrics": {
    "mw": 138.12, "logp_crippen": 1.09, "tpsa": 57.5,
    "hbd": 2, "hba": 3, "rotb": 1, "heavy_atoms": 10, "rings": 1,
    "logS": -1.8
  },
  "satisfies_constraints": true,
  "constraint_failures": [],
  "score": -1.8,
  "workflow_uuid": "...",
  "metric_path_used": "solubilities.O.solubilities.0",
  "object_data": { "...": "full Rowan response saved verbatim" }
}

Failed candidates record score: null and an error field — they remain in the log so the agent can see and avoid the same failure mode next time.

See docs/file-formats.md for the complete schemas.

Composite Objective Example

Reward solubility, lightly penalize molecular weight, all in one scalar objective_score:

uv run rowan-state init \
  --run aspirin_balanced \
  --objective "Maximize aqueous logS while mildly penalizing molecular weight" \
  --direction maximize \
  --metric objective_score \
  --workflow solubility \
  --start-smiles "CC(=O)Oc1ccccc1C(=O)O" \
  --workflow-param method=kingfisher \
  --workflow-param solvents='["water"]' \
  --workflow-param temperatures='[298.15]' \
  --objective-term '{"name":"logS","path":"solubilities.O.solubilities.0","goal":"maximize","weight":1.0}' \
  --objective-term '{"name":"mw","source":"local","metric":"mw","goal":"minimize","weight":0.01}'

Term contributions are saved per candidate under optimization_objective, so you can audit how each term influenced the ranking.

Batch Docking Example

uv run rowan-state init \
  --run kinase_batch_docking \
  --objective "Minimize Rowan docking score against the kinase pocket while keeping ligands drug-like" \
  --direction minimize \
  --metric docking_score \
  --workflow batch_docking \
  --start-smiles "c1ccccc1" \
  --workflow-param protein=<rowan-protein-uuid> \
  --workflow-param pocket='[[0.0,0.0,0.0],[12.0,12.0,12.0]]' \
  --workflow-param executable=qvina2 \
  --workflow-param scoring_function=vina \
  --workflow-param exhaustiveness=8 \
  --constraint mw_max=500 --constraint logp_max=5 \
  --constraint hbd_max=5 --constraint hba_max=10 \
  --constraint rotb_max=10

batch_docking submits the whole iteration's candidate list as one Rowan batch workflow — the right choice for scoring tens of ligands against a fixed pocket.

Tips For Productive Runs

Always score the starting molecule first for a new workflow/metric to verify metric extraction. If score is null, open the latest iteration JSON, inspect object_data, and pin the right metric_path in config.json.
Keep most candidates close to the current best. Use the parent SMILES field to record provenance — it drives the genealogy plot.
Diversify each batch. Pair a polar swap with a steric move and an electronic move so one Rowan submission tests several plausible ideas.
Respect constraints before scoring. RDKit descriptors are cheap; don't spend Rowan credits on a candidate that already busts MW.
Use rationales like a lab notebook. Future-you (and reviewers) should be able to read the report top-to-bottom and follow the chemistry.
Mark convergence explicitly. Use --decision converged or --decision stuck on the final scoring iteration so the report is honest.

Full agent protocol: AGENTS.md.

Documentation

The top-level README is a feature overview. For the full handbook, see docs/README.md:

Getting started — setup, baseline scoring.
Optimization loop — what each iteration looks like.
Run configuration — every config.json field.
Workflows and metrics — built-in extractors and how to pin custom paths.
Scoring and reporting — candidate format, parallelism, decisions, plots.
File formats — concrete JSON schemas.
Examples — worked use cases.
Troubleshooting — API keys, invalid SMILES, metric-path misses, constraint failures, Rowan errors.

Current Limitations

The optimizer is external by design. The AI agent is the proposer; there is no autonomous BO/GA loop. rowan-suggest adds an advisory surrogate/active-learning layer (dedup + Expected-Improvement ranking + diversity), but it only ranks the agent's proposals — it never submits to Rowan on its own.
Reports center on one scalar score. Composite objectives are recorded per term in JSON, but plots show only the combined score for now.
candidates_per_iter and max_iterations are guidance for the agent; the CLI does not hard-enforce either.
Workflows requiring non-SMILES inputs (proteins, pockets, spin states, reaction endpoints) work but are not validated against current Rowan SDK signatures before submission — make sure workflow_params matches the Rowan submitter you're targeting.
Local constraints are RDKit descriptors only. Rowan-output constraints (e.g. "pKa must be 4–6") are expressible as composite objective terms.

Name		Name	Last commit message	Last commit date
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.github/workflows		.github/workflows
docs		docs
examples		examples
rowan_tools		rowan_tools
templates		templates
tests		tests
viz		viz
.env.example		.env.example
.gitignore		.gitignore
AGENTS.md		AGENTS.md
README.md		README.md
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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Rowan Autosearch

Who This Is For

What You Get

How The Loop Works

Features

Rowan workflows supported

Workflow parameter passthrough

Metric extraction

Composite (weighted) objectives

Local RDKit constraints (drug-likeness gates)

Parallel scoring + batch docking

Brainstorming helper (rowan-propose)

Surrogate active-learning advisor (rowan-suggest)

Auditable HTML report

Append-only audit trail

Setup

5-Minute Quickstart: aqueous solubility from aspirin

Use Cases & Recipes

Command Reference

rowan-state init — start a run

rowan-state status / rowan-state list

rowan-score — score candidates

rowan-report — rebuild plots + HTML

rowan-propose — brainstorming helper

rowan-suggest — surrogate active-learning advisor

Run Outputs

Composite Objective Example

Batch Docking Example

Tips For Productive Runs

Documentation

Current Limitations

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