AI-assisted chemical reaction impurity prediction and propagation

22 September 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Most chemical reactions result in numerous by-products and side-products, apart from the intended major product. While chemists can predict many of the main process impurities, it remains a challenge to enumerate the possible minor impurities and even more of a challenge to systematically predict and track impurities derived from raw materials or those that have propagated from one synthetic step to the next. In this study, we developed an AI-assisted approach to predict and track impurities across multi-step reactions using the main reactants, and optionally reagents, solvents and impurities in these materials, as input. We demonstrated the utility of this tool for a simple case of synthesis of paracetamol from phenol, and provide a generalized framework that covers most chemical reactions. Our solution can be applied to enable (1) faster elucidation of impurities, (2) automated interpretation of data generated from high-throughput reaction screening, and (3) more thorough raw materials risk assessments, with each of these representing key workflows in small molecule drug substance commercial process development.


impurity identification
process development
automated impurity prediction
impurity structure elucidation
synthesis route selection
synthesis route optimization
chemical process development


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