Evolutionary Retrosynthetic Route Planning

TL;DR

This paper introduces a novel evolutionary algorithm-based method for multi-step molecular retrosynthesis, significantly improving search efficiency and solution diversity compared to traditional Monte Carlo tree search methods.

Contribution

It is the first application of evolutionary algorithms to multi-step retrosynthesis, modeling the problem as an optimization task and implementing a parallel strategy for efficiency.

Findings

EA reduces single-step model calls by 53.9% on average

Search time decreases by 83.9% on average

Feasible search routes increase by 1.38 times

Abstract

Molecular retrosynthesis is a significant and complex problem in the field of chemistry, however, traditional manual synthesis methods not only need well-trained experts but also are time-consuming. With the development of big data and machine learning, artificial intelligence (AI) based retrosynthesis is attracting more attention and has become a valuable tool for molecular retrosynthesis. At present, Monte Carlo tree search is a mainstream search framework employed to address this problem. Nevertheless, its search efficiency is compromised by its large search space. Therefore, this paper proposes a novel approach for retrosynthetic route planning based on evolutionary optimization, marking the first use of Evolutionary Algorithm (EA) in the field of multi-step retrosynthesis. The proposed method involves modeling the retrosynthetic problem into an optimization problem, defining the search space and operators. Additionally, to improve the search efficiency, a parallel strategy is implemented. The new approach is applied to four case products and compared with Monte Carlo tree search. The experimental results show that, in comparison to the Monte Carlo tree search algorithm, EA significantly reduces the number of calling single-step model by an average of 53.9%. The time required to search three solutions decreases by an average of 83.9%, and the number of feasible search routes increases by 1.38 times. The source code is available at https://github.com/ilog-ecnu/EvoRRP.