Network search algorithms and scoring functions for advanced‐level computerized synthesis planning

In 2020, a “hybrid” expert‐AI computer program called Chematica (a.k.a. Synthia) was shown to autonomously plan multistep syntheses of complex natural products, which remain outside the reach of purely data‐driven AI programs. The ability to plan at this level of chemical sophistication has been attributed mainly to the superior quality of Chematica's reactions rules. However, rules alone are not sufficient for advanced synthetic planning which also requires appropriately crafted algorithms with which to intelligently navigate the enormous networks of synthetic possibilities, score the synthetic positions encountered, and rank the pathways identified. Chematica's algorithms are distinct from prêt‐à‐porter algorithmic solutions and are product of multiple rounds of improvements, against target structures of increasing complexity. Since descriptions of these improvements have been scattered among several of our prior publications, the aim of the current Review is to narrate the development process in a more comprehensive manner. This article is categorized under: Data Science > Computer Algorithms and Programming Data Science > Artificial Intelligence/Machine Learning Quantum Computing > Algorithms A network view of one‐step synthetic options leading to a target molecule. In realistic retrosynthetic searches for complex targets, networks comprised of tens of thousands of such “spiders” are examined.