Gradient Propagation in Retrosynthetic Space: An Efficient Framework for Synthesis Plan Generation

TL;DR

This paper introduces a gradient-propagation-based framework for retrosynthesis that efficiently explores chemical space by identifying key nodes contributing to synthesis success, outperforming existing methods in computational efficiency.

Contribution

The paper presents a novel gradient propagation approach for retrosynthetic route search, addressing uncertainties and improving efficiency over prior algorithms.

Findings

Achieves broad applicability across diverse molecular targets.

Demonstrates superior computational efficiency.

Effectively guides search by identifying key nodes.

Abstract

Retrosynthesis, which aims to identify viable synthetic pathways for target molecules by decomposing them into simpler precursors, is often treated as a search problem. However, its complexity arises from multi-branched tree-structured pathways rather than linear paths. Some algorithms have been successfully applied in this task, but they either overlook the uncertainties inherent in chemical space or face limitations in practical application scenarios. To address these challenges, this paper introduces a novel gradient-propagation-based algorithmic framework for retrosynthetic route exploration. The proposed framework obtains the contributions of different nodes to the target molecule's success probability through gradient propagation and then guides the algorithm to greedily select the node with the highest contribution for expansion, thereby conducting efficient search in the chemical space. Experimental validations demonstrate that our algorithm achieves broad applicability across diverse molecular targets and exhibits superior computational efficiency compared to existing methods.