Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflow

Rowan: Cloud Native Molecular Modeling and Drug Design Workflows Overview Rowan is a cloud native workflow platform for molecular simulation, medicinal chemistry, and structure based design. Its Python API exposes a unified interface for small molecule modeling, property prediction, docking, molecular dynamics, and AI structure workflows. Use Rowan when you want to run medicinal chemistry or molecular design workflows programmatically without maintaining local HPC infrastructure, GPU provisioning, or a collection of separate modeling tools. Rowan handles all infrastructure, result management, and computation scaling. When to use Rowan Rowan is a good fit for: Quantum chemistry, semiempirical methods, or neural network potentials Batch property prediction (pKa, descriptors, permeability, solubility) Conformer and tautomer ensemble generation Docking workflows (single ligand, analogue series, pose refinement) Protein ligand cofolding and MSA generation Multi step chemistry pipelines (e.g., tautomer search → docking → pose analysis) Batch medicinal chemistry campaigns where you need consistent, scalable infrastructure Rowan is not the right fit for: Simple molecular I/O (use RDKit directly) Post HF ab initio quantum chemistry or relativistic calculations Quick start If that prints without error, you're set up correctly. These values and examples were verified against rowan python 3.1.13. Installation User and webhook management Authentication Set an API key via environment variable (recommended): Or set directly in Python: Verify authentication: Molecule input formats Rowan accepts molecules in the following formats: SMILES (preferred): "CCO" , "c1ccccc1O" SMARTS patterns (for some workflows): subset of SMARTS for substructure matching InChI (if supported in your API version): "InChI=1S/C2H6O/c1 2 3/h3H,2H2,1H3" The API validates molecule inputs and raises ValueError for an unparseable SMILES or a workflow incompatible input type. Always use canonicalized SMILES for reproducibility. SMILES strings versus molecule objects Accepted input types vary by workflow in rowan python 3.1.13. Only these common workflows accept a bare string: pKa, conformer search, membrane permeability, ADMET, LogP, macropKa, solubility, and pose analysis MD. Most others — including descriptors, tautomer search, docking, analogue docking, BDE, NMR, and Fukui — require rowan.Molecule.from smiles(smiles) or an RDKit Mol / RWMol . A wrong type raises ValueError before submission. Tip: Use RDKit to validate SMILES before submission: Core usage pattern Most Rowan tasks follow the same three step pattern: 1. Submit a workflow 2. Wait for completion (with optional streaming) 3. Retrieve typed results with convenience properties For long running workflows, use streaming: result() vs. stream result() Pattern Use When Duration result() You can wait for the full result <5 min typical stream result() You want progress feedback or need early partial results 5 min, or interactive use Guideline: Use result() for descriptors, pKa. Use stream result() for conformer search, docking, cofolding. Working with results Rowan's API includes typed workflow result objects with convenience properties. Using typed properties and .data Results have two access patterns: 1. Convenience properties (recommended first): result.descriptors , result.best pose , result.scores . Result classes differ: conformer search uses get energies() and get conformers() methods. 2. Raw fallback : result.data — raw dictionary from the API Example: Note: DescriptorsResult does not have a molecular weight property. MW is exact/monoisotopic mass, not average molecular weight. TPSA is a 3D charged surface descriptor; use TopoPSA for the usual topological polar surface area used in drug likeness rules. Cache invalidation Some result properties are lazily loaded (e.g., conformer geometries, protein structures). To refresh: Projects, folders, and organization For nontrivial campaigns, use projects and folders to keep work organized. Projects Folders Workflow decision trees pKa vs. MacropKa Use microscopic pKa when: You need the pKa of a single ionizable group You're interested in acid–base transitions and protonation thermodynamics The molecule has one or two ionizable sites Speed is critical (faster, fewer credits) Use macropKa when: You need pH dependent behavior across a physiologically relevant range (e.g., 0–14) You want aggregated charge and protonation state populations across pH The molecule has multiple ionizable groups with coupled protonation You need downstream properties like aqueous solubility at different pH Example decision: Conformer search vs. tautomer search Use conformer search when: A single tautomeric form is known You need a diverse 3D ensemble for docking, MD, or SAR analysis Rotatable bonds dominate the chemical space Use tautomer search when: Tautomeric equilibrium is uncertain (e.g., heterocycles, keto–enol systems) You need to model all relevant protonation isomers Downstream calculations (docking, pKa) depend on tautomeric form Combined workflow: Docking vs. analogue docking vs. cofolding Workflow Use When Input Output Docking Single ligand, known pocket Protein + SMILES + pocket coords Pose, score, dG Analogue docking 5–100+ related compounds Protein + SMILES list + reference ligand All poses, reference aligned Protein ligand cofolding Sequence + ligand, no crystal structure Protein sequence + SMILES ML predicted bound complex Protein utilities Upload proteins Protein preparation guidance File format : PDB, mmCIF (Rowan auto detects) Water molecules : Rowan usually keeps relevant water; remove bulk water beforehand if desired Heteroatoms : Cofactors, ions, and bound ligands are usually preserved; remove unwanted heteroatoms before upload Multi chain proteins : Fully supported Resolution : Works with NMR structures, homology models, and cryo EM; quality matters for downstream predictions Validation : Rowan validates PDB syntax; severely malformed files may be rejected Workflow catalog Nine common workflow categories — descriptors, microscopic pKa, MacropKa, conformer search, tautomer search, docking, analogue docking, MSA generation, and protein ligand cofolding — each with submission code and result shapes, plus the complete list of every supported workflow type (core modeling, structure based design, advanced computational chemistry, reaction chemistry, advanced properties, binding free energy, and sequence and structural biology) are in [references/workflow catalog.md](references/workflow catalog.md). Batch submission, webhooks, and asynchronous work Batch submit/poll/retrieve, the non blocking fire and check pattern, webhook setup, secret creation and rotation, payload and signature verification (with a FastAPI handler), and webhook best practices are in [references/batch and webhooks.md](references/batch and webhooks.md). Access, pricing, and credits Free tier limits, credit consumption per workflow, and typical cost estimates are in [references/access and pricing.md](references/access and pricing.md). Worked example and troubleshooting A full lead optimization campaign — project setup, tautomers, pKa across an analogue series, result collection, and a docking follow up — is in [references/end to end example.md](references/end to end example.md). Common errors with their fixes, and debugging tips, are in [references/troubleshooting.md](references/troubleshooting.md). Recommended usage patterns Prefer Rowan native workflows over low level assembly when they exist Use projects and folders for any nontrivial campaign ( 5 workflows) Use result() to block until complete (default: wait=True, poll interval=5 ) Use typed result properties first , fall back to .data for unmapped fields Use batch submission for compound libraries or analogue series Chain workflows for multi step chemistry campaigns: pKa → macropKa → permeability (ADME assessment) tautomer search → docking → pose analysis MD (pose refinement) MSA generation → protein ligand cofolding (AI structure prediction) Use webhooks for long running campaigns ( 50 workflows) or asynchronous pipelines Use streaming for interactive feedback on large conformer/docking searches Summary Use Rowan when your workflow requires cloud execution for molecular design tasks, especially when you want one unified API and consistent result handling across small molecule modeling, proteins, docking, ADME prediction, and ML structure generation. Rowan is a molecular design workflow platform, not just a remote chemistry engine. It handles infrastructure scaling, result persistence, and multi step pipeline orchestration so you can focus on science.