Enhancing Reaction Yield Predictions with Machine Learning Models for Organic Synthesis

Abstract

The prediction of reaction yields stems from organic synthesis processes, which is key in the enhancement of sophisticated and economical chemical processes. Innovations in machine learning (ML) models, specifically in artificial neural networks (ANNs) and deep learning, have greatly improved the ease and accuracy of yield prediction. The focus of this paper is the use of various ML models in predicting reaction yields for organic synthesis with emphasis on their ability to exacerbate the reaction conditions’ selection such as solvent, catalyst, and temperature. Through the automated analysis of vast datasets of chemical reactions, machine learning models are able to detect correlations and trends that would be difficult to find using conventional techniques. The combination of AI and experimental chemistry provides an efficient and modernized means of predicting reaction outcomes, thus minimizing the amount of time and resources needed to conduct experiments with unknown results. This paper demonstrates the feasibility and emerging effectiveness of machine learning for the prediction of response yields together with some of the major hurdles to its implementation and formulates some suggestions for achieving greater precision and wider applicability of the models in real synthetic chemistry.

Keywords: Machine Learning, Organic Synthesis, Reaction Yield Prediction, Artificial Neural Networks, Deep Learning, Chemical Reactions, Catalyst Optimization, Solvent Selection, Predictive Models, Synthetic Chemistry.