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

14 May 2024, Version 4
This content is a preprint and has not undergone peer review at the time of posting.


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.


Small molecule synthesis
Small data
Prediction of reaction conditions
in-silico data generation

Supplementary materials

Supporting Information
1. General information 2. Synthesis and characterization of substrate 3. Preparation of magnesium amide bases 4. Iodination reaction of polyfluoronaphthalenes 5. Computational studies 6. References Appendix 1. Cartesian coordinates Appendix 2. Details of predicted yields Appendix 3. List of algorithms


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