Powerful and Reliable Prediction using Latent Variables of Experimentally Unobservable Reactions in Organic Synthesis

02 August 2024, Version 6
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

In this study, a novel machine learning algorithm was designed to assist in the development of organic reactions. This 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 is used to estimate untested yields via extrapolation. An approach based on Bayesian optimization and dual annealing optimization is employed to compute expected values and evaluate plausibility. The algorithm’s dual-loop 2 structure, incorporating latent 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 perfluoroiodinated naphthalenes is demonstrated. The algorithm exhibits potential for application in predicting experimentally unobservable reactions, thereby advancing the field of synthetic organic chemistry.

Keywords

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

Supplementary materials

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Supporting Information
Description
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 Appendix 4. List of descriptors
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