Freecurve provides a physics‑integrated AI platform that predicts thermodynamic properties, solvation free energies, protein‑ligand binding affinities, lithium‑ion electrolyte behavior, enzyme reaction intermediates, and metal processing outcomes with near‑experimental accuracy
Funding
Funding not disclosed
Founders
Product
Problem
Accurately predicting thermodynamic properties of liquids, solvation energies, protein‑ligand binding, and battery electrolyte behavior requires costly experiments or computational methods that lack chemical accuracy, limiting the speed of drug, energy, and materials discovery.
Solution
Freecurve offers a physics‑integrated AI platform that generates predictive Boltzmann ensembles for a wide range of chemical systems. By training on quantum‑level data from dimers and leveraging fundamental physics, the models achieve near‑experimental correlation (≈0.98) for solvation free energies, partition coefficients, and protein‑ligand binding energies. The same framework predicts lithium‑ion solvation in electrolytes, enzyme reaction intermediates, and metal processing outcomes with errors as low as 2‑4 % of measured values. Users obtain high‑accuracy property predictions without running laboratory experiments, enabling rapid iteration in drug design, battery optimization, and catalyst development.
Target Audience
Primary customers are pharmaceutical and biotech companies, battery and energy storage developers, and materials scientists seeking high‑accuracy computational predictions for drug discovery, electrolyte formulation, and catalyst optimization.
Features
- Predictive models for solvation free energies, partition coefficients, and other liquid properties with 0.98 correlation to experimental data
- Protein‑ligand binding energy predictions (e.g., PARP, MCL1, CDK2, Thrombin) within medicinal‑chemist error margins
- Lithium‑ion solvation and electrolyte behavior modeling accurate to within 2 % of measurements
- Enzyme reaction intermediate energy predictions with 2 % accuracy, supporting catalyst design
- Metal processing forecasts (e.g., copper ore enrichment) accurate within 4 %, potentially increasing yields by 5‑10 %
- Quantum‑trained AI using only dimer calculations, eliminating the need for large-scale DFT clusters
- Cloud‑based API delivering Boltzmann ensemble predictions for integration into drug, energy, and materials pipelines