Crystal-XG
Crystal-XG provides an AI-powered platform that predicts optimal cocrystal synthesis conditions, including solvent, method, temperature, molar ratio, and crystallization time. The machine-learning platform replaces traditional trial-and-error experimentation, reducing solvent waste and laboratory resource consumption for crystallography researchers and pharmaceutical R&D teams.
- Chemical Technology
- Artificial Intelligence
- Clean Technology
- New Materials
- Drug Discovery & Therapeutics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Cocrystal synthesis is time-consuming and environmentally harmful, as conventional methods rely on large volumes of organic solvents that contribute to environmental pollution. Traditional approaches are resource-intensive, requiring expensive reagents and specialized equipment, and depend on trial-and-error experimentation that slows research progress.
Solution
Crystal-XG provides an AI-powered platform that harnesses machine learning to make reaction condition predictions faster and more reliable for crystallography research. The platform predicts optimal experimental parameters including solvent selection, synthesis method, temperature, molar ratio, and expected days to crystallize, replacing guesswork with data-driven guidance. This approach simplifies complex structure research, enhances data accuracy, and accelerates discovery timelines while reducing solvent waste and laboratory resource consumption.
Target Audience
Primary users are crystallography researchers and pharmaceutical or materials science R&D teams who need faster, more efficient cocrystal synthesis with reduced laboratory resource consumption.
Features
- Machine learning-driven prediction of crystallization conditions across five key parameters: solvent, method, temperature, molar ratio, and days to crystallize
- AI platform designed specifically to eliminate the environmental burden of organic solvent waste in cocrystal synthesis
- Predictive modeling that replaces traditional trial-and-error experiments with data-backed recommendations
- Solutions targeting the full workflow from reaction setup through crystallization timeline estimation