When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges.

ABSTRACT: Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inhe

SUBMITTER: Voinarovska V 

PROVIDER: S-EPMC10778086 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature