Title: Beyond Search: Direct Model Guidance for Steerable Synthesis Planning
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: retrosynthesis, synthesis planning, steerable chemical synthesis, classifier-guidance, sequence-to-sequence models

Abstract: Synthetic chemists often need to incorporate domain-specific constraints and preferences into synthesis plans, 
such as preferred reaction types or available starting materials, motivating the development of steerable synthesis planning methods. 
While previous approaches navigate the search space with a frozen single-step model for plausible multi-step routes, 
we demonstrate that directly guiding the single-step retrosynthesis model enables exploration of previously inaccessible chemical 
spaces during generation. Specifically, we invoke guidance to modify the token-level logits of an autoregressive seq2seq retrosynthesis model, 
enabling conditioning on various properties without retraining. 
Empirically, we demonstrate that while previous multi-step methods struggle to find routes with chemically feasible single-step 
reactions throughout the entire synthesis plan, our method generates synthesis routes of equal or better quality than 
template-based approaches while satisfying the specified constraints, 
thus narrowing the long-standing gap between template-based and data-driven synthesis planning.

TL;DR: We apply classifier guidance to a seq2seq retrosynthesis model to steer synthesis planning toward desired properties, enabling exploration of new chemical spaces without retraining.