ArticleCommunications chemistry2023
Retrosynthetic planning with experience-guided Monte Carlo tree search.
Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- AI-Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery.Medicinal research reviews · 2026Review
- The rise of agentic AI in drug discovery from static tools to autonomous virtual scientists.npj drug discovery · 2026Review
- Digital twin based sorting optimization for parcel distribution centers in logistics networks.Nature communications · 2026Article
- Hyp-retro: hypernetwork-guided preference-conditioned retrosynthetic planner.Molecular diversity · 2026Article
- Retrosynthetic crosstalk between single-step reaction and multi-step planning.Journal of cheminformatics · 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
- Review
- A data-driven group retrosynthesis planning model inspired by neurosymbolic programming.Nature communications · 2025Article
- Article
- DrugGym: A testbed for the economics of autonomous drug discovery.bioRxiv : the preprint server for biology · 2024Article
- AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application.Journal of cheminformatics · 2024Article
- Efficient retrosynthetic planning with MCTS exploration enhanced ACommunications chemistry · 2024Article
- Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery.Chemical science · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In retrosynthetic planning, the huge number of possible routes to synthesize a complex molecule using simple building blocks leads to a combinatorial explosion of possibilities. Even experienced chemists often have difficulty to select the most promising transformations. The current approaches rely on human-defined or machine-trained score functions which have limited chemical knowledge or use expensive estimation methods for guiding. Here we propose an experience-guided Monte Carlo tree search (EG-MCTS) to deal with this problem. Instead of rollout, we build an experience guidance network to learn knowledge from synthetic experiences during the search. Experiments on benchmark USPTO datasets show that, EG-MCTS gains significant improvement over state-of-the-art approaches both in efficiency and effectiveness. In a comparative experiment with the literature, our computer-generated routes mostly matched the reported routes. Routes designed for real drug compounds exhibit the effectiveness of EG-MCTS on assisting chemists performing retrosynthetic analysis.
Identifiers
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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.