The
field of computer-aided synthesis planning (CASP) has witnessed
significant growth in recent years. Still, many CASP programs rely
on large data sets to train neural networks, resulting in limitations
due to the data quality and prior knowledge from chemists. In response,
we propose Retrosynthesis Zero (ReSynZ), a reaction template-based
method that combines Monte Carlo Tree Search with reinforcement learning
inspired by AlphaGo Zero. Unlike other single-step reaction template-based
CASP methods, ReSynZ takes complete synthesis paths for complex molecules,
determined by reaction rules, as input for training the neural network.
ReSynZ enables neural networks trained with relatively small reaction
data sets (tens of thousands of data) to generate multiple synthesis
pathways for a target molecule and suggest possible reaction conditions.
On multiple data sets of molecular retrosynthesis, ReSynZ demonstrates
excellent predictive performance compared to existing algorithms.
The advantages, such as self-improving model features, flexible reward
settings, the potential to surpass human limitations in chemical synthesis
route planning, and others, make ReSynZ a valuable tool in chemical
synthesis design.