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Accelerated Chemical Reaction Optimization Using Multi-Task Learning by Connor J. Taylor; Kobi C. Felton; Daniel Wigh; Mohammed I. Jeraal; Rachel Grainger; Gianni Chessari; Christopher N. Johnson; Alexei A. Lapkin is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is Accelerated Chemical Reaction Optimization Using Multi-Task Learning about?
Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several __in silico__ case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C–H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to
Who reads Accelerated Chemical Reaction Optimization Using Multi-Task Learning?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Connor J. Taylor; Kobi C. Felton; Daniel Wigh; Mohammed I. Jeraal; Rachel Grainger; Gianni Chessari; Christopher N. Johnson; Alexei A. Lapkin
- Publisher
- American Chemical Society (ACS)
- Published
- 2023
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Physical Sciences)