Virtual Variables as Powerful and Reliable Parameters to Predict Experimentally Unobservable Reactions in Organic Synthesis

This study introduces a novel machine learning algorithm designed to assist in the development of organic reactions in organic synthesis. The algorithm addresses the complexities inherent in batch-type organic reactions, including the necessity for numerous experiments and the effects of intricate characteristics of reaction pathways. By integrating molecular relationships and actual yields from observable reactions, the algorithm estimates virtual yields through extrapolation. A Bayesian optimization-based approach is employed to compute expected values and evaluate plausibility. The algorithm’s dual-loop structure, incorporating virtual variables and experimental values, maximizes the coefficient of determination. Physicochemical aspects of the algorithm are validated using natural bond orbital charges, and its utility in synthesizing perfluoro iodinated naphthalenes is demonstrated. The algorithm exhibits potential for predicting experimentally unobservable reactions, thereby advancing organic chemistry.