SmartChemistry® Platform
Welcome to the ChemAI Platform page: your gateway to exploring the SmartChemistry® suite of AI-powered tools designed to accelerate chemical R&D. This page provides insights into modules like Retrosynthesis, Optimizer, Outcome Prediction, and Robotics, each aimed at enhancing synthesis planning, experiment design, and automation. Click the links for further information on each product. Navigate through the sections to understand how ChemAI’s integrated platform can streamline your research processes and drive innovation in your projects.

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.

ChemAI’s SmartChemistry® platform enhances synthesis planning by combining proprietary reaction data with advanced AI modeling to deliver high-confidence predictions for small-molecule reactions. Its mechanism-aware models, trained on curated datasets, empower chemists to assess feasibility, compare routes, and optimize conditions before entering the lab.

ChemAI’s predictive models integrate with automated synthesis and robotics platforms to enable closed-loop experimentation and real-time decision-making. By forecasting outcomes like yield, selectivity, and feasibility, the technology prioritizes high-value experiments, reducing iterations and enhancing the efficiency of high-throughput and autonomous labs – advancing robotics from simple automation to intelligent chemical exploration.