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 severalin silicocase 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.