Once the reaction scheme components are all present and marked up, the last step in this toolchain is to propose a coarse-grained set of conditions, namely the duration, temperature and catalyst concentration that is likely to provide the best yield.
Continue readingSometimes the best way to validate a prediction is to go check the literature and look for the most similar examples, to see if it looks reasonable. Fortunately the data that these reaction prediction tools are based only mostly comes with a DOI attached, so this feature can be added automagically.
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The process of completing a reaction scheme includes four preliminary co-dependent steps: proposing formal byproducts, obtaining pairwise atom-to-atom mappings, balancing the stoichiometry, and assigning roles to each of the reaction components.
Continue readingPrediction 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.
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In this chapter we will explore models that can propose and rank solvents for a partially specified reaction. The methodology uses graph-based deep learning models trained on a moderate sized corpus of very well curated reactions with each of the solvents represented as a chemical structure.
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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 