ArticleChemical science2020
Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- CycloPepper: a machine learning platform for predicting cyclization outcomes and optimizing synthesis of therapeutic cyclopeptides.Nature communications · 2026Article
- Green Drug Discovery: Leveraging Biodiversity for Sustainable Pharmaceutical Solutions.Chemistry & biodiversity · 2026Review
- Data-driven recommendation of agents, temperature, and equivalence ratios for organic synthesis.Chemical science · 2025Article
- The Promise and Pitfalls of Artificial Intelligence in the Evaluation of Synthetic "Greenness".ChemSusChem · 2025Article
- Enhancing Monte Carlo Tree Search for Retrosynthesis.Journal of chemical information and modeling · 2025Article
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- ASKCOS: Open-Source, Data-Driven Synthesis Planning.Accounts of chemical research · 2025Article
- ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning.Nature communications · 2024Article
- Deep learning in template-free de novo biosynthetic pathway design of natural products.Briefings in bioinformatics · 2024Review
- Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.ACS synthetic biology · 2024Article
- New sustainable polymers and oligomers for Cultural Heritage conservation.Chemical science · 2024Article
- Autonomous and dynamic precursor selection for solid-state materials synthesis.Nature communications · 2023Article
- Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search.Chemical science · 2023Article
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Retrosynthetic planning with experience-guided Monte Carlo tree search.Communications chemistry · 2023Article
- Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning.Journal of cheminformatics · 2023Article
- Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery.Chemical science · 2023Review
- Exploring Optimal Reaction Conditions Guided by Graph Neural Networks and Bayesian Optimization.ACS omega · 2022Article
- Machine Learning Guides Peptide Nucleic Acid Flow Synthesis and Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge.Journal of chemical information and modeling · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer aided synthesis planning of synthetic pathways with green process conditions has become of increasing importance in organic chemistry, but the large search space inherent in synthesis planning and the difficulty in predicting reaction conditions make it a significant challenge. We introduce a new Monte Carlo Tree Search (MCTS) variant that promotes balance between exploration and exploitation across the synthesis space. Together with a value network trained from reinforcement learning and a solvent-prediction neural network, our algorithm is comparable to the best MCTS variant (PUCT, similar to Google's Alpha Go) in finding valid synthesis pathways within a fixed searching time, and superior in identifying shorter routes with greener solvents under the same search conditions. In addition, with the same root compound visit count, our algorithm outperforms the PUCT MCTS by 16% in terms of determining successful routes. Overall the success rate is improved by 19.7% compared to the upper confidence bound applied to trees (UCT) MCTS method. Moreover, we improve 71.4% of the routes proposed by the PUCT MCTS variant in pathway length and choices of green solvents. The approach generally enables including Green Chemistry considerations in computer aided synthesis planning with potential applications in process development for fine chemicals or pharmaceuticals.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.