ConstruQt – Trusted Molecular Ensembles for AI and Computational Chemistry
Know your molecules better. Generate physics-grounded ensembles and descriptors for machine learning and predictive modeling. Try NowValue Proposition
Physics-based data, not rules-based guesses
ConstruQt produces accurate molecular ensembles using quantum mechanics and Boltzmann-weighted energy rankings. Going beyond simple 2D descriptors to generate 3D structures, electronic properties, and high-fidelity descriptors that power downstream predictive models and AI workflows.
Key Benefits
Reduce Label Noise
Reduce label noise in ML datasets for cleaner results.
Capture Variants
Capture medicinal-chemistry-relevant tautomers, protomers, and stereoisomers.
Detect Errors
Spot data inconsistencies automatically.
Scalable Generation
Generate ensembles and descriptors at scale.
How it works
High-fidelity molecular ensembles at scale
ConstruQt integrates with existing pipelines via JSON-RPC API or through our own web interface.
Submit molecules and receive:
Boltzmann-weighted molecular ensembles
2D, 3D, and electronic descriptors
Provenance metadata
Boltzmann-weighted molecular ensembles
2D, 3D, and electronic descriptors
Provenance metadata
Uses cases:
ML Input Data:
Physics-based input data for machine learning models.
Experimental QA:
Curation and quality assurance of experimental chemical databases.
Model Validation:
Benchmarking and validation against physics-based standards.
Scalability & Integration
From pilot to millions of molecules
Massive Throughput
Scale to thousands of cores, millions of molecules per day.
Scientific Reliability
Semi-empirical and QM-backed calculations with >98% success rate.
Seamless Connectivity
API-first design for integration into existing ML and cheminformatics pipelines.
Bridging the Gap Between Chemistry and AI
ConstruQt is the physics layer for data — transforming experimental and synthetic chemistry data into trustworthy, machine-ready molecular ensembles.
From Complex Chemistry to Streamlined Data
Experience the power of the physics layer through an interface designed for clarity—managing experimental reactions and transforming them into structured, high-fidelity datasets ready for AI integration.
Get in touch for trial licenses and pilot projects
Access pilot ensembles, enterprise integration, and design-partner programs.
