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 among the reported synthetic protocols may be successful.
For example, Michael β-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 (Support Vector Machine,
Naive Bayes, and Random Forest) in combination with different types
of descriptors (ISIDA fragments issued from Condensed Graphs of Reactions,
MOLMAP, Electronic Effect Descriptors, and Chemistry Development Kit
computed descriptors) have been used. Models have good predictive
performance in 3-fold cross-validation done three times: balanced
accuracy varies from 0.7 to 1. Developed models are available for
the users at http://infochim.u-strasbg.fr/webserv/VSEngine.html. Eventually, these were challenged to predict feasibility conditions
for ∼50 novel Michael reactions from the eNovalys database
(originally from patent literature).