  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.
