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Updated: Sep 18, 2025
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Continuous Flow Chemistry: Reaction of Diphenyldiazomethane with p-Nitrobenzoic Acid
Published on: November 15, 2017
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Machine Learning-Driven Optimization of Continuous-Flow Photoredox Amine Synthesis
Perman Jorayev1,2, Sebastian Soritz1,3, Simon Sung2
1Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom.
Organic Process Research & Development
|June 26, 2025
Summary
Machine learning optimized photoredox catalysis for synthesizing tertiary amines. This approach significantly improved reaction efficiency and throughput compared to traditional batch methods, enabling faster drug discovery.
Area of Science:
- Organic Chemistry
- Catalysis
- Chemical Engineering
Background:
- Photoredox catalysis is crucial for synthesizing pharmaceutically relevant C-(sp3)-rich tertiary amines.
- Optimizing these reactions is challenging due to complex mechanistic models and vast reaction spaces.
Purpose of the Study:
- To demonstrate machine learning-driven optimization for photoredox tertiary amine synthesis.
- To identify key reaction parameters and improve process robustness in a continuous flow setup.
Main Methods:
- Utilized a semiautomated continuous flow setup with six continuous and one discrete variable.
- Employed a priori knowledge generation (e.g., solubility predictions) and a Bayesian optimization algorithm (NEMO).
- Analyzed results using permutation feature importance and partial dependence plots.
Main Results:
- Identified critical parameters influencing yield and cost, including catalyst loading, residence time, and solvent choice.
- Discovered correlations between catalyst loading, residence time, and absorbed photon equivalence.
- Achieved a throughput approximately 25 times higher than batch reactions, reaching ~12 g/day.
Conclusions:
- Machine learning, particularly NEMO, effectively optimizes complex photoredox reactions.
- Continuous flow synthesis offers significant productivity gains over batch processes for tertiary amine synthesis.
- The developed workflow accelerates the discovery and optimization of valuable pharmaceutical intermediates.
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