Bayesian Optimization

Bayesian Optimization is a machine learning-powered tool within the ChemAIRS® platform from Chemical.AI, designed to help synthetic chemists intelligently explore and exploit reaction space. Rather than relying on slow, manual trial-and-error experimentation, it learns from past experimental data to recommend the most promising next experiment — reaching optimal reaction conditions in fewer rounds, with less material waste and reduced cost.

The tool is built for chemists facing the challenge of optimizing reactions across thousands of possible variable combinations, including catalysts, ligands, solvents, temperature, and time. By replacing guesswork with data-driven recommendations, ChemAIRS' Bayesian Optimization accelerates timelines, reduces failed experiments, and ensures meaningful progress with every round run.

Why Traditional Optimization Falls Short

• Manual, literature-driven approaches require weeks or months of iterative experimentation
• High material costs accumulate from failed or uninformative experiments
• Delayed timelines slow down scale-up decisions
• Significant effort is expended with little meaningful progress toward optimal conditions

Distinctive Features

• Automatic intelligent starting point: ChemAIRS automatically recommends the initial set of experiments, providing a strong, data-driven starting point without manual guesswork
• Learns from internal data: The model can leverage your historical reaction data, learning from past experiments to make smarter and faster recommendations
• Integrated with retrosynthesis: Bayesian Optimization is fully integrated with the ChemAIRS retrosynthesis module, so optimized conditions fit naturally into your synthetic planning workflow
• Faster learning, fewer experiments: By combining smart initialization, internal data, and retrosynthetic context, the tool reaches optimal conditions in fewer rounds — saving time, materials, and budget

How It Works: Step-by-Step Workflow

1. Define Your Reaction Parameters: Choose from discrete variables and screen any reaction variable, from catalysts and ligands to temperature and time. Include reagent equivalencies to simultaneously optimize catalyst loading, solvent volume, and more. Material costs can also be incorporated to plan for future scale-up budgets.
2. Define Your Objectives: Set optimization goals such as maximizing conversion, yield, or selectivity, or minimizing cost and material usage. Use single or multi-parameter objectives to balance performance, efficiency, and budget simultaneously.
3. Run Your Experiments: The Bayesian optimization algorithm adapts to the results of each round, continuously guiding the campaign toward the global optimum. Experimental results are visualized after each round, and the workflow can be paired with high-throughput experimentation (HTE) instrumentation to further accelerate optimization.

Bayesian Optimization is exclusively available as part of the ChemAIRS® platform, where it is uniquely integrated with retrosynthesis capabilities. This combination ensures that optimized reaction conditions are directly connected to synthetic planning, making it a comprehensive solution for modern reaction development workflows.