---
title: "AOT* — paper by Song et al. (2026) — summary and ideas"
description: "It asks an LLM to propose complete multi-step routes for unsolved molecules and grafts each valid… 79 references, cited by 2 papers here."
authors: ["Xiaozhuang Song", "Xuanhao Pan", "Xinjian Zhao", "Hangting Ye", "Shufei Zhang", "Jian Tang", "Tianshu Yu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1734/"
doi: "10.18653/v1/2026.findings-acl.1734"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/song-2026-aot-efficient-synthesis-planning-llm/"
---
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# AOT\*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search

**Authors:** Xiaozhuang Song, Xuanhao Pan, Xinjian Zhao, Hangting Ye, [Shufei Zhang](https://md.research.elamur.ai/free/en/authors/shufei-zhang/), [Jian Tang](https://md.research.elamur.ai/free/en/authors/jian-tang/) and Tianshu Yu · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1734/) · [PDF](https://aclanthology.org/2026.findings-acl.1734.pdf) · [DOI 10.18653/v1/2026.findings-acl.1734](https://doi.org/10.18653/v1/2026.findings-acl.1734) · [OpenAlex](https://openalex.org/W7166818745)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 2 in our library, of the 149,439 papers whose bibliographies we read

## Summary
AOT* is a method for multi-step retrosynthesis planning, which means finding a chain of reactions that breaks a target molecule down into purchasable building blocks. Existing neural and LLM-based planners can be costly. LLM-Syn-Planner, for example, refines whole routes through evolutionary search and needs many expensive LLM calls. AOT* takes a different approach. It asks an LLM to propose complete multi-step routes for unsolved molecules and grafts each valid route onto a classical AND-OR tree, where OR nodes are molecules and AND nodes are reactions. Canonical SMILES ensure each molecule appears only once in the tree, so intermediates found earlier can be reused. Route steps that target molecules already solved are dropped. Leaf reactions to expand next are chosen with a UCB rule. Prompts include a few similar known routes, retrieved by Tanimoto similarity on Morgan fingerprints, and reaction templates filter out invalid chemistry. Across benchmarks, AOT* reaches competitive or better solve rates in 3–5× fewer iterations, with larger gains on hard targets. This makes LLM-driven synthesis planning more practical when compute or API budgets are limited.

## Key points
- On Pistachio Hard with DeepSeek-V3, AOT* solves 67% of targets at 20 iterations and 86% at 100. Under the same budgets, LLM-Syn-Planner reaches 13% and 74%. GPT-4o shows a similar pattern: 64%/85% versus 9%/72%.
- Retrieval matters but saturates quickly. Without retrieved examples the solve rate is 60%, and with 3 examples it is 86%. Going up to 20 examples adds only about 2 points while increasing tokens by 177% (1,478 → 4,091).
- The gains hold across five LLMs: GPT-4o-mini, DeepSeek-V3, GPT-4o, Claude-4-Sonnet and Gemini-2.5 Pro. DeepSeek-V3 gives the best balance of cost and performance.
- The method is not very sensitive to hyperparameters. Solve rates stay between 77% and 86%, with the best setting at exploration constant c = 0.5 and temperature 0.7.
- The authors list several limitations. Results depend on the LLM's chemical knowledge and on the quality of retrieved examples, so performance may drop for complex natural products and novel chemical spaces. The method has no multi-objective search or uncertainty estimates yet. Code is available at github.com/ShawnKS/AOTstar.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Xiaozhuang Song, Xuanhao Pan, Xinjian Zhao, Hangting Ye, Shufei Zhang, Jian Tang and Tianshu Yu. 2026. AOT\*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{song2026aot,
  title = {{AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search}},
  author = {Xiaozhuang Song, Xuanhao Pan, Xinjian Zhao, Hangting Ye, Shufei Zhang, Jian Tang and Tianshu Yu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1734},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1734}
}
```

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75. Situo Zhang, Hanqi Li, Lu Chen, Zihan Zhao, Xuanze Lin, Zichen Zhu, Bo Chen, Xin Chen, and Kai Yu. 2025b. Reasoning-driven retrosynthesis prediction with large language models via reinforcement learning. arXiv preprint arXiv:2507.17448. — [arXiv:2507.17448](https://arxiv.org/abs/2507.17448) [find](https://scholar.google.com/scholar?q=Situo+Zhang%2C+Hanqi+Li%2C+Lu+Chen%2C+Zihan+Zhao%2C+Xuanze+Lin%2C+Zichen+Zhu%2C+Bo+Chen%2C+Xin+Chen%2C+and+Kai+Yu%2E+2025b%2E+Reasoning%2Ddriven+retrosynthesis+prediction+with+large+language+models+via+reinforcement+learni)
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*Updated 31 October 2026*