Using models to propose recommendations for chemical reactions is appropriate for many of the steps needed to fill out the scheme, but sometimes it’s more effective to pick an existing reaction and use it as a template to build out the missing pieces. This approach can be applied to forward and backward syntheses (starting from a reactant or product respectively) and also to finding and proposing stoichiometric reagents.
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Reaction Prediction Models: Chapter 3 – Confidence
Prediction models for proposing and ranking catalysts and solvents are all very well, but some predictions are more reliable than others. Coming up with some kind of metric for evaluating the difference is a major improvement to utility.
Continue readingReaction Prediction Models: Chapter 1 – Catalysts
In this chapter we will explore models that can propose and rank catalysts for a given reaction transform. The methodology uses graph-based deep learning models trained on a moderate sized corpus of very well curated reactions, each of which has the catalyst molecule (or set of molecules) drawn with a chemically meaningful structure.
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Cheminformatics 2.0 