MolClass

MolClass builds and serves machine learning models that predict small-molecule bioactivity — training supervised classifiers on your own SDF datasets, then searching, browsing, and predicting against them through a modern web UI, REST API, or MCP tools for agentic/programmatic workflows.

Give it a labeled set of molecules (active/inactive, or any categorical property), and it computes chemical descriptors and fingerprints, trains and evaluates a classifier, and puts the result behind a human approval gate before it's used for predictions. Everything after that — searching your compound library, predicting on new molecules, reviewing model evidence — is available through the web app, the REST API, or directly from an AI assistant via the bundled MCP server.

Why it's worth a look

• Benchmarked, not just built. A default RandomForest-on-JUMBO configuration matches DeepTox — the winning method of the Tox21 Data Challenge — without any deep-learning infrastructure. See Details in the app, or /news, for the writeup.
• A real chemistry pipeline, not a thin wrapper. Descriptor and fingerprint generation runs on the Chemistry Development Kit (CDK) — 200+ molecular descriptors plus 8 fingerprint families (MACCS, PubChem, Extended, Substructure, Klekota-Roth, Graph-only, EState, ECFP4) — computed deterministically and versioned per molecule, not recomputed ad hoc on every request.
• Eleven classifiers, two feature-selection strategies, via Weka: Random Forest, J48, Naive Bayes, SMO, k-NN, LibSVM, Logistic Model Tree, LogitBoost, a stacked ensemble, Bagged J48, and AdaBoost.M1 — with correlation-based (CFS) or ReliefF feature selection, or none.
• A human release gate, not a training-equals-deploy pipeline. Every model build is immutable evidence (holdout/validation/train metrics, manifest hash, artifact checksums) that a named reviewer must explicitly approve — recorded via a canonical, audited transaction — before it's ever used for a prediction.
• Structure search that's actually structure-aware: exact match and substructure search by SMILES, not just text matching on names.
• A featured-molecule handoff, not a dead-end showcase. The landing page's "MolClass molecule of the month" card opens structure search with the molecule's canonical SMILES already loaded and drawn. From there it can be found in the registry or registered and explored against approved MolClass models. The current feature is BMS-820132, an investigational oral partial glucokinase activator developed for type 2 diabetes (IUPHAR/BPS Guide to PHARMACOLOGY).
• Talk to it from an AI assistant. mcp-server/ exposes search and prediction as MCP tools, including registering a brand-new molecule from a raw SMILES string and predicting on it in one call — useful for wiring MolClass into an agent workflow instead of a browser.

Architecture

A modern, containerized stack — seven Docker Compose services:

Service	What it does
frontend	Next.js / React web UI
api	FastAPI REST service — datasets, uploads, model definitions, review
sdf-worker	Imports and analyzes uploaded SDF files (Java, CDK)
model-worker	Trains and evaluates models (Java, CDK + Weka)
molecule-worker	Registers ad-hoc molecules and runs one-off predictions
predictor	Spring Boot service serving predictions and structure search
db	MariaDB

Chemistry and machine learning run on the JVM (CDK + Weka); the API and web layers are Python (FastAPI) and TypeScript (Next.js). Everything is wired together through a versioned job queue, so long-running work (import, feature generation, model training) is resumable and auditable rather than a fire-and-forget background thread.

Getting started

See INSTALL.md for the full guide. The short version:

git clone https://github.com/jwildenhain/molclass.git
cd molclass
cp .env.example .env   # fill in MOLCLASS_DB_ROOT_PASSWORD and MOLCLASS_DB_PASSWORD
docker compose build
docker compose up -d

Then open http://127.0.0.1:3000.

Local database dumps, dataset snapshots, and deployment safety copies belong under backups/. That directory is intentionally ignored and must not be added to Git history.

Citation

If you use MolClass in your work, please cite:

Wildenhain J, Fitzgerald N, Tyers M. Bioinformatics. 2012 Aug 15;28(16):2200-1.

Contact

Jan Wildenhain — LinkedIn