On the influence of template size, canonicalization and exclusivity for retrosynthesis and reaction prediction applications

Heuristic and machine learning models for rank-ordering reaction templates comprise an important basis for computer-aided organic synthesis regarding both product prediction and retrosynthetic pathway planning. Their viability relies heavily on the quality and characteristics of the underlying template database. With the advent of automated reaction and template extraction software and consequently the creation of template databases too large to be curated manually, a data-driven approach to assess and improve the quality of template sets is needed. We therefore systematically studied the influence of template generality, canonicalization and exclusivity on the performance of different template ranking models. We find that duplicate and non-exclusive templates, \textit{i.e.} templates which describe the same chemical transformation on identical or overlapping sets of molecules, decrease both the accuracy of the ranking algorithm and the applicability of the respective top-ranked templates significantly. To remedy the negative effects of non-exclusivity, we developed a general and computationally efficient framework to deduplicate and hierarchically correct templates. As a result, performance improved for both heuristic and machine learning template ranking algorithms across different template sizes. The canonicalization and correction code was made freely available.