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Accelerated Chemical Reaction Optimization Using Multi-Task Learning.

Published version
Peer-reviewed

Repository DOI


Type

Article

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Authors

Abstract

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 industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization.

Description

Keywords

3404 Medicinal and Biomolecular Chemistry, 34 Chemical Sciences, Generic health relevance

Journal Title

ACS Cent Sci

Conference Name

Journal ISSN

2374-7943
2374-7951

Volume Title

9

Publisher

American Chemical Society (ACS)
Sponsorship
Engineering and Physical Sciences Research Council (EP/S024220/1)