SmartChemistry® Platform
ChemAI’s core AI model, trained on over 23 million chemical reactions (including 4 million proprietary entries) – enabling advanced molecular predictions, reaction simulations, and material discoveries. By leveraging deep learning and proprietary data, it enhances accuracy in property predictions, accelerates drug discovery, and optimizes chemical synthesis. Integrated with ChemAI’s platform, it provides researchers with powerful, scalable tools for AI-driven innovation in pharmaceuticals, materials science, and green chemistry
SmartChemistry® Curation transforms unstructured chemical data – such as reports from CROs, CDMOs, and ELNs – into structured, machine-readable formats. By leveraging advanced AI and cheminformatics, it enables seamless integration of chemical reaction data for machine learning, automation, and AI applications, enhancing the efficiency and accuracy of chemical R&D workflows.
ChemAI’s AI-driven platform designed to predict reaction yields with over 90% accuracy in medicinal and process chemistry. By integrating machine learning, Bayesian optimization, and automated LC/MS analysis, it enables chemists to identify high-impact experiments, reduce waste, and accelerate discovery. The platform has demonstrated success in complex amide coupling reactions and aims to extend its capabilities across the top 20 pharmaceutical transformations.
SmartChemistry® Retrosynthesis is ChemAI’s AI-driven tool that streamlines synthetic route planning by integrating curated chemical knowledge with advanced algorithms. It enables chemists to rapidly explore and compare viable synthesis pathways, enhancing efficiency from discovery to production.
The SmartChemistry® Optimizer is ChemAI’s AI-powered tool for accelerating chemical R&D through intelligent experiment design, predictive modeling, and multi-parameter optimization – helping chemists achieve better outcomes with fewer experiments.
ChemAI’s technology enables in-depth outcome prediction for small-molecule reactions, delivering critical insights across the entire synthesis planning and optimization process. Users can predict key reaction outcomes such as likelihood of product formation, expected yield, regioselectivity, stereoselectivity, and potential byproduct formation – all informed by proprietary, mechanism-aware AI models trained on unique chemical datasets. These predictions help chemists assess feasibility, compare alternative routes, and select optimal conditions before committing resources to the lab.
By integrating predictive modeling directly into R&D workflows, ChemAI empowers scientists to make more informed, confident decisions, reduce experimental waste, and accelerate discovery and development timelines.
ChemAI’s predictive models integrate seamlessly with automated synthesis and robotics platforms, enabling closed-loop experimentation with real-time decision-making. By forecasting reaction outcomes – such as yield, selectivity, and feasibility – our technology helps prioritize the most promising experiments, reducing the number of iterations required to reach optimal results.
This data-driven approach enhances the efficiency of high-throughput experimentation and autonomous labs, allowing robotics systems to work smarter, not just faster. Whether driving automated reaction optimization or enabling hands-free synthesis development, ChemAI transforms robotics from automation to intelligent exploration.
