Multi-objective synthesis planning by means of Monte Carlo Tree search - Helen Lai; Christos Kannas; Alan Kai Hassen; Emma Granqvist; Annie M. Westerlund; Djork-Arné Clevert; Mike Preuss; Samuel Genheden (Artificial Intelligence in the Life Sciences)
We introduce a multi-objective search algorithm for retrosynthesis planning, based on a Monte Carlo Tree search formalism. The multi-objective search allows 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 range 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 can outperform the single-objective search and provides a more diverse set of solutions. However, for many target compounds, the single- and multi-objective settings are equivalent. Nevertheless, our algorithm provides a framework for incorporating novel objectives for specific applications in synthesis planning.