biostudies-literatureUnknown9(5)Taylor CJFunctionalization 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 <i>in silico</i> 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 cheACS central science957-968https://www.ebi.ac.uk/biostudies/studies/S-EPMC10214532biostudies-literatureAccelerated Chemical Reaction Optimization Using Multi-Task Learning.PMC10214532Taylor CJGrainger RJeraal MILapkin AAWigh DFelton KCChessari GJohnson CNfalseAccelerated Chemical Reaction Optimization Using Multi-Task Learning.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 <i>in silico</i> 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 che2023-01-01T00:00:00Z2023 May2025-04-22T22:00:42.008Z2025-04-06T03:53:10.563ZS-EPMC102145323725234810.1021/acscentsci.3c00050