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 allows for combining diverse set of objectives without considering their scale or weighting factors. To benchmark this novel algorithm, we employ four...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Artificial Intelligence in the Life Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667318525000066 |
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| author | Helen Lai Christos Kannas Alan Kai Hassen Emma Granqvist Annie M. Westerlund Djork-Arné Clevert Mike Preuss Samuel Genheden |
| author_facet | Helen Lai Christos Kannas Alan Kai Hassen Emma Granqvist Annie M. Westerlund Djork-Arné Clevert Mike Preuss Samuel Genheden |
| author_sort | Helen Lai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1938e87f3454471fba17dfb16ab3f144 |
| institution | OA Journals |
| issn | 2667-3185 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Artificial Intelligence in the Life Sciences |
| spelling | doaj-art-1938e87f3454471fba17dfb16ab3f1442025-08-20T02:33:12ZengElsevierArtificial Intelligence in the Life Sciences2667-31852025-06-01710013010.1016/j.ailsci.2025.100130Multi-objective synthesis planning by means of Monte Carlo Tree searchHelen Lai0Christos Kannas1Alan Kai Hassen2Emma Granqvist3Annie M. Westerlund4Djork-Arné Clevert5Mike Preuss6Samuel Genheden7Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK; Corresponding author.Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, SwedenLeiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands; Machine Learning Research, Pfizer Research and Development, Berlin, GermanyMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden; Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, SwedenMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, SwedenMachine Learning Research, Pfizer Research and Development, Berlin, GermanyLeiden Institute of Advanced Computer Science, Leiden University, Leiden, The NetherlandsMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, SwedenWe 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.http://www.sciencedirect.com/science/article/pii/S2667318525000066Monte Carlo Tree searchPareto optimalityMulti-objective optimizationReinforcement learningMarkov Decision ProcessPolicy network |
| spellingShingle | Helen Lai Christos Kannas Alan Kai Hassen Emma Granqvist Annie M. Westerlund Djork-Arné Clevert Mike Preuss Samuel Genheden Multi-objective synthesis planning by means of Monte Carlo Tree search Artificial Intelligence in the Life Sciences Monte Carlo Tree search Pareto optimality Multi-objective optimization Reinforcement learning Markov Decision Process Policy network |
| title | Multi-objective synthesis planning by means of Monte Carlo Tree search |
| title_full | Multi-objective synthesis planning by means of Monte Carlo Tree search |
| title_fullStr | Multi-objective synthesis planning by means of Monte Carlo Tree search |
| title_full_unstemmed | Multi-objective synthesis planning by means of Monte Carlo Tree search |
| title_short | Multi-objective synthesis planning by means of Monte Carlo Tree search |
| title_sort | multi objective synthesis planning by means of monte carlo tree search |
| topic | Monte Carlo Tree search Pareto optimality Multi-objective optimization Reinforcement learning Markov Decision Process Policy network |
| url | http://www.sciencedirect.com/science/article/pii/S2667318525000066 |
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