Multi-Objective Synthesis Planning by Means of Monte Carlo Tree Search

We introduce a multi-objective search algorithm for retrosynthesis planning, based on a Monte Carlo Tree search formalism. The multi-objective search allow for combining diverse set of objectives without considering their scale or weighting factors. To benchmark this novel algorithm, we employ four objectives in a total of eight retrosynthesis experiments on a PaRoutes benchmark set. The objectives ranges from simple ones based on starting material and step count to complex ones based on synthesis complexity and route similarity. We show that with the careful employment of complex objectives, the multi-objective algorithm outperforms the single-objective search and provides a more diverse set of solution that is closer to the desired objective. Our algorithm thus provides a framework for incorporating novel objectives for specific applications in synthesis planning.