# AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search

arXiv: https://arxiv.org/abs/2509.20988 · HTML: https://mlsanity.com/paper/2509.20988/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 96 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 30 of 32)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction](https://mlsanity.com/paper/1811.02633/) (2018)
- [What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks](https://mlsanity.com/paper/2305.18365/) (2023)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks](https://mlsanity.com/paper/2402.01817/) (2024)
- [Translation between Molecules and Natural Language](https://mlsanity.com/paper/2204.11817/) (2022)
- [ChemLLM: A Chemical Large Language Model](https://mlsanity.com/paper/2402.06852/) (2024)
- [Retrosynthesis Prediction with Conditional Graph Logic Network](https://mlsanity.com/paper/2001.01408/) (2020)
- [Retrosynthetic reaction prediction using neural sequence-to-sequence models](https://mlsanity.com/paper/1706.01643/) (2017)
- [A Graph to Graphs Framework for Retrosynthesis Prediction](https://mlsanity.com/paper/2003.12725/) (2020)
- [Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search](https://mlsanity.com/paper/2006.15820/) (2020)
- [Learning Graph Models for Retrosynthesis Prediction](https://mlsanity.com/paper/2006.07038/) (2020)
- [Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2405.14170/) (2024)
- [Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning](https://mlsanity.com/paper/2410.04223/) (2024)
- [Re-evaluating Retrosynthesis Algorithms with Syntheseus](https://mlsanity.com/paper/2310.19796/) (2023)
- [Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search](https://mlsanity.com/paper/2407.06334/) (2024)
- [Self-Improved Retrosynthetic Planning](https://mlsanity.com/paper/2106.04880/) (2021)
- [Retrosynthetic Planning with Dual Value Networks](https://mlsanity.com/paper/2301.13755/) (2023)
- [Retro-fallback: retrosynthetic planning in an uncertain world](https://mlsanity.com/paper/2310.09270/) (2023)
- [Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search](https://mlsanity.com/paper/2112.06028/) (2021)
- [LLM-Augmented Chemical Synthesis and Design Decision Programs](https://mlsanity.com/paper/2505.07027/) (2025)
- [MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning](https://mlsanity.com/paper/2210.02630/) (2022)
- [BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction](https://mlsanity.com/paper/2408.10285/) (2024)
- [Enhancing Chemical Reaction and Retrosynthesis Prediction with Large Language Model and Dual-task Learning](https://mlsanity.com/paper/2505.02639/) (2025)
- [Tango*: Constrained synthesis planning using chemically informed value functions](https://mlsanity.com/paper/2412.03424/) (2024)
- [DeepRetro: Retrosynthetic Pathway Discovery using Iterative LLM Reasoning](https://mlsanity.com/paper/2507.07060/) (2025)

## Built on by (most cited first, 13 of 13)

- [Synthelite: Chemist-aligned and feasibility-aware synthesis planning with LLMs](https://mlsanity.com/paper/2512.16424/) (2025)
- [SynthStrategy: Extracting and Formalizing Latent Strategic Insights from LLMs in Organic Chemistry](https://mlsanity.com/paper/2512.01507/) (2025)
- [Towards Agentic Intelligence for Materials Science](https://mlsanity.com/paper/2602.00169/) (2026)
- [Towards Diverse Scientific Hypothesis Search with Large Language Models](https://mlsanity.com/paper/2606.10587/) (2026)
- [RetroAgent: Harnessing LLMs to Search Over Structured Memory for Agentic Retrosynthesis Planning](https://mlsanity.com/paper/2607.14512/) (2026)
- [Project Ariadne: Prompt-Conditioned Route Generation for Synthesis Planning](https://mlsanity.com/paper/2606.24184/) (2026)
- [Closed-Loop Molecular Inverse Design with Semantic LLM Surrogates](https://mlsanity.com/paper/2608.22967/) (2026)
- [RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction](https://mlsanity.com/paper/2603.12666/) (2026)
- [Reinforced Reasoning for End-to-End Retrosynthetic Planning](https://mlsanity.com/paper/2603.29723/) (2026)
- [An Agentic Retrobiosynthesis Framework with Learned Frontier Selection](https://mlsanity.com/paper/2608.30702/) (2026)
- [RetroGEF: Dynamic Graph Edit Flow for Single-Step Retrosynthesis](https://mlsanity.com/paper/2609.38484/) (2026)
- [Coverage Before Control: Route-Instruction Grounding and Steering for Controllable Retrosynthesis](https://mlsanity.com/paper/2609.39955/) (2026)
- [Latent JEPA: Abstract Future Prediction for Latent Reasoning in Chemistry](https://mlsanity.com/paper/2610.01947/) (2026)

## Abstract (arXiv)

Retrosynthesis planning enables the discovery of viable synthetic routes for target molecules, playing a crucial role in domains like drug discovery and materials design. Multi-step retrosynthetic planning remains computationally challenging due to exponential search spaces and inference costs. While Large Language Models (LLMs) demonstrate chemical reasoning capabilities, their application to synthesis planning faces constraints on efficiency and cost. To address these challenges, we introduce AOT*, a framework that transforms retrosynthetic planning by integrating LLM-generated chemical synthesis pathways with systematic AND-OR tree search. To this end, AOT* atomically maps the generated complete synthesis routes onto AND-OR tree components, with a mathematically sound design of reward assignment strategy and retrieval-based context engineering, thus enabling LLMs to efficiently navigate in the chemical space. Experimental evaluation on multiple synthesis benchmarks demonstrates that AOT* achieves SOTA performance with significantly improved search efficiency. AOT* exhibits competitive solve rates using 3-5× fewer iterations than existing LLM-based approaches, with the efficiency advantage becoming more pronounced on complex molecular targets.