Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Previous approaches are not high-throughput, are not generalizable or scalable, or lack sufficient data to be effective. We describe single mechanistic reactions as concerted electron movements from an electron orbital source to an electron orbital sink. We use an existing rule-based expert system to derive a dataset consisting of 2,989 productive mechanistic steps and 6.14 million non-productive mechanistic steps. We then pose identifying productive mechanistic steps as a ranking problem: rank potential orbital interactions such that the top ranked interactions yield the major products.

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Previous approaches are not high-throughput, are not generalizable or scalable, or lack sufficient data to be effective. We describe single mechanistic reactions as concerted electron movements from an electron orbital source to an electron orbital sink. We use an existing rule-based expert system to derive a dataset consisting of 2,989 productive mechanistic steps and 6.14 million non-productive mechanistic steps. We then pose identifying productive mechanistic steps as a ranking problem: rank potential orbital interactions such that the top ranked interactions yield the major products.

In their paper uploaded to the preprint server arXiv, the group outlines their approach, which they describe as an improvement over other models. Predicting what will happen when chemicals are mixed or treated in certain ways is difficult because of all the variables involved. But scientists would like to have a tool that does it anyway, because it would dramatically speed up development of useful new materials, especially drugs. In this new effort, the team at IBM has taken an entirely new approach to creating such a tool. The new approach involves treating chemical reactions as a translation problem by rephrasing elements in such predictions as letters and words rather than atoms and molecules.