Towards an expert system for predicting reaction conditions: the Michael reaction case

A generic chemical transformation may often be achieved under various synthetic conditions. However, for any specific reagents, only one or a few amongst the reported synthetic protocols may be successful. For example, Michael beta-addition reactions may proceed under different choices of solvent (e.g., hydrophobic, aprotic polar, protic) and catalyst (e.g., Brønsted acid, Lewis acid, Lewis base, etc.). Chemoinformatics methods could be efficiently used to establish a relationship between the reagent structures and the required reaction conditions, which would allow synthetic chemists to waste less time and resources in trying out various protocols in search for the appropriate one. In order to address this problem, a number of 2-classes classification models have been built on a set of 198 Michael reactions retrieved from literature. Trained models discriminate between processes that are compatible and respectively processes not feasible under a specific reaction condition option (feasible or not with a Lewis acid catalyst, feasible or not in hydrophobic solvent, etc). Eight distinct models were built to decide the compatibility of a Michael addition process with each considered reaction condition option, while a ninth model was aimed to predict whether the assumed Michael addition is feasible at all. Different machine-learning methods (SVM, Naive Bayes and Random Forest) in combination with different types of descriptors have been used. In particular, the ChemAxon toolkit was used to develop Electronic Effect Descriptors (EED), that are particularly well suited in this context. Obtained models have a reasonable predictive performance in 3 times 3-fold cross- validation: Balanced Accuracy varies from 0.7 to 1.

Developed models are available for the users over here. Eventually, these were challenged to predict feasibility conditions for ~50 novel Michael reactions from the eNovalys database (originally retrieved from patent literature).

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