Multi-objective synthesis planning by means of Monte Carlo Tree search
Artikel i vetenskaplig tidskrift, 2025

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.

Multi-objective optimization

Reinforcement learning

Monte Carlo Tree search

Pareto optimality

Policy network

Markov Decision Process

Tree edit distance

Författare

Helen Lai

AstraZeneca AB

Christos Kannas

AstraZeneca AB

Alan Kai Hassen

Pfizer

Universiteit Leiden

Emma Granqvist

AstraZeneca AB

Chalmers, Data- och informationsteknik, Data Science och AI

Annie M. Westerlund

AstraZeneca AB

Djork Arné Clevert

Pfizer

Mike Preuss

Universiteit Leiden

Samuel Genheden

AstraZeneca AB

Artificial Intelligence in the Life Sciences

26673185 (eISSN)

Vol. 7 100130

Ämneskategorier (SSIF 2025)

Matematik

Data- och informationsvetenskap (Datateknik)

DOI

10.1016/j.ailsci.2025.100130

Mer information

Senast uppdaterat

2025-03-06