Aqueous Solubility Predictor

Overview

Estimate logS with an ESOL-style RDKit descriptor heuristic and classify aqueous solubility. It is in the Prediction + Cheminformatics category and is intended to estimate molecular properties and cheminformatics relationships before deeper modeling or experiment.

When To Use It

• You need a focused workflow for aqueous solubility predictor without leaving ChemistryAtlas.
• You want a result that can be saved, shared, or chained into another chemistry app.
• You want the calculation assumptions and limitations visible next to the output.

Inputs

• text - Chemistry input - type: textarea - Use formulas, names, SMILES-like text, reactions, or key=value options. Heavier engines will plug into this same app surface.

Recommended Workflow

• Submit one molecule or a small set of molecules, inspect descriptor and alert tables, and use the result to decide whether to continue, redesign, or collect more data.
• Start with the smallest representative input, confirm the parser understood it, then scale to a larger list or workflow.
• Save the generated report when the result will feed a notebook entry, route review, model comparison, or team discussion.

Outputs

• A Markdown-style chemistry report with parsed inputs, assumptions, and calculated or predicted results.
• Structured tables when the app returns multiple compounds, reagents, routes, peaks, candidates, or model rows.
• Warnings, fallback notes, and sidecar availability messages when a specialized engine is not installed or not reachable.

Method And Backend Notes

This app has a runnable ChemistryAtlas backend path. Backend type: model. ChemistryAtlas roadmap MVP: runnable report now; specialist cheminformatics/model backend plugs into this app surface next. Use the output as a structured starting point for chemistry judgment, follow-up calculation, or experimental planning.

How To Interpret Results

• Look for trends and flags rather than single-value certainty; predictions are most useful when compared across analogs.
• Compare results across related molecules, controls, blanks, literature examples, or known reactions whenever possible.
• For decisions that affect safety, synthesis scale-up, biological testing, purchasing, or publication, verify with primary data and expert review.

Example Input

caffeine
CCCCCCCCCCCCCCCC

Common Checks Before Acting

• Confirm names, salts, stereochemistry, tautomers, protonation state, and hydration state.
• Check units, concentrations, equivalent definitions, and significant figures.
• Record external database versions, model versions, sidecar availability, and any manual edits made after the app output.

Related Apps

• ADMET / Drug-Likeness Panel (admet-drug-likeness-panel)
• Structural Alerts + Synthetic Accessibility (structural-alerts-sascore)
• QSAR / ML Model Builder (qsar-ml-model-builder)
• Active-Learning Experiment Recommender (active-learning-experiment-recommender)
• Descriptor / Fingerprint Generator (descriptor-fingerprint-generator)

Acknowledgements And Validation

• ChemistryAtlas documentation and UI were prepared for chemistry discovery workflows.
• Where available, calculations may use open-source cheminformatics, reaction-informatics, spectra, docking, or machine-learning engines such as RDKit-family tooling, ASKCOS-style sidecars, ChemProp, ms-pred/ICEBERG, PyScreener, and MolPAL.
• Always verify important results against primary literature, official SDS records, instrument software, validated models, and local laboratory procedures.
• Model-driven outputs should include model version, training domain, uncertainty, and independent validation before operational use.