SynOmega: Simplifying Retrosynthesis for Efficient Synthesizability Scoring
TL;DR
Synthesis-based synthesizability scoring triages molecules from generative design but is expensive: every score requires a multi-step search, so SynOmega offers a cheap, data-level action-space constraint that makes route-based synthesizability scoring more efficient without sacrificing solvability.
Abstract
Retrosynthesis-based synthesizability scoring triages molecules from generative design but is expensive: every score requires a multi-step search. We present SynOmega, an open-source toolkit that couples a single-step template model, an AND–OR route search, and a route-based synthesizability score (SynScore). Its single-step model can be restricted, at the reaction-template level, to simplifying disconnections that split the target into smaller precursors. On 1000 ChEMBL drug molecules this yields two findings. (1) The simplifying constraint cuts node expansions by about 30% on jointly solved targets and lowers the median search time by about a third. This reduction in search effort is the robust, budget-independent result; the small accompanying rise in solved rate is a secondary effect between the two separately trained models, not the isolated result of toggling one model’s action space. (2) As a complete system, under matched search depth, width and iteration budget, SynOmega reaches about 1.8× the solved rate of the open-source planner AiZynthFinder while searching about 13× faster. SynOmega thus offers a cheap, data-level action-space constraint that makes route-based synthesizability scoring more efficient without sacrificing solvability.