TempRe: Template generation for single and direct multi-step retrosynthesis

TL;DR

TempRe introduces a novel sequence generation framework for retrosynthesis, improving scalability, flexibility, and chemical plausibility, and demonstrating superior performance on multi-step benchmarks and direct route generation.

Contribution

It reformulates template-based retrosynthesis as sequence generation, enabling scalable and flexible synthesis planning with strong empirical results.

Findings

TempRe outperforms existing methods on multi-step benchmarks.

It achieves high top-k route accuracy in retrosynthesis tasks.

TempRe efficiently generates direct multi-step synthesis routes.

Abstract

Retrosynthesis planning remains a central challenge in molecular discovery due to the vast and complex chemical reaction space. While traditional template-based methods offer tractability, they suffer from poor scalability and limited generalization, and template-free generative approaches risk generating invalid reactions. In this work, we propose TempRe, a generative framework that reformulates template-based approaches as sequence generation, enabling scalable, flexible, and chemically plausible retrosynthesis. We evaluated TempRe across single-step and multi-step retrosynthesis tasks, demonstrating its superiority over both template classification and SMILES-based generation methods. On the PaRoutes multi-step benchmark, TempRe achieves strong top-k route accuracy. Furthermore, we extend TempRe to direct multi-step synthesis route generation, providing a lightweight and efficient alternative to conventional single-step and search-based approaches. These results highlight the potential of template generative modeling as a powerful paradigm in computer-aided synthesis planning.