There are two main approaches to drawing reactions: the canvas model, where everything is drawn onto a flat page, and the component model, where each reaction participant is treated as a discrete object. Both of these approaches have important advantages, and significant problems which have to be addressed.
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Reaction Prediction Models: Chapter 7 – Backward/Forward Synthesis and Reagents
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.
Continue readingReaction Prediction Models: Chapter 6 – Yield Contours
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 readingReaction Prediction Models: Chapter 4 – Byproducts, Mapping, Balancing and Roles
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 readingReaction 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 2 – Solvents
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.
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.
Continue readingReaction Prediction Models: Chapter 0
This is the first article in a series about chemical reaction prediction, in particular a work-in-progress site that combines a number of original tools for designing reactions. The general idea is that you start with an incomplete reaction scheme, and the models and algorithms will guide you toward filling out the rest, so you end up with a useful starting point for an actual experiment.
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Cheminformatics 2.0 