# Synthelite: Chemist-aligned and feasibility-aware synthesis planning with LLMs

arXiv: https://arxiv.org/abs/2512.16424 · HTML: https://mlsanity.com/paper/2512.16424/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 57 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction](https://mlsanity.com/paper/1811.02633/) (2018)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [Translation between Molecules and Natural Language](https://mlsanity.com/paper/2204.11817/) (2022)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery](https://mlsanity.com/paper/2505.13259/) (2025)
- [Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search](https://mlsanity.com/paper/2006.15820/) (2020)
- [ChessGPT: Bridging Policy Learning and Language Modeling](https://mlsanity.com/paper/2306.09200/) (2023)
- [Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction](https://mlsanity.com/paper/2110.09681/) (2021)
- [Training a Scientific Reasoning Model for Chemistry](https://mlsanity.com/paper/2506.17238/) (2025)
- [Puzzle Solving using Reasoning of Large Language Models: A Survey](https://mlsanity.com/paper/2402.11291/) (2024)
- [Can Large Language Models Empower Molecular Property Prediction?](https://mlsanity.com/paper/2307.07443/) (2023)
- [Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis](https://mlsanity.com/paper/2311.10776/) (2023)
- [Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search](https://mlsanity.com/paper/2407.06334/) (2024)
- [Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis Planning](https://mlsanity.com/paper/2308.05522/) (2023)
- [LLM-Augmented Chemical Synthesis and Design Decision Programs](https://mlsanity.com/paper/2505.07027/) (2025)
- [AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search](https://mlsanity.com/paper/2509.20988/) (2025)
- [Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity](https://mlsanity.com/paper/2410.03138/) (2024)
- [LARC: Towards Human-level Constrained Retrosynthesis Planning through an Agentic Framework](https://mlsanity.com/paper/2508.11860/) (2025)
- [Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry](https://mlsanity.com/paper/2411.15221/) (2024)
- [Tango*: Constrained synthesis planning using chemically informed value functions](https://mlsanity.com/paper/2412.03424/) (2024)
- [Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration](https://mlsanity.com/paper/2510.16590/) (2025)

## Built on by (most cited first, 10 of 10)

- [When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs](https://mlsanity.com/paper/2602.03554/) (2026)
- [MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models](https://mlsanity.com/paper/2512.21231/) (2025)
- [URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment](https://mlsanity.com/paper/2607.04688/) (2026)
- [Mimosa Framework: Toward Evolving Multi-Agent Systems for Scientific Research](https://mlsanity.com/paper/2603.28986/) (2026)
- [MMORF: A Multi-agent Framework for Designing Multi-objective Retrosynthesis Planning Systems](https://mlsanity.com/paper/2604.05075/) (2026)
- [Project Ariadne: Prompt-Conditioned Route Generation for Synthesis Planning](https://mlsanity.com/paper/2606.24184/) (2026)
- [Agentic generation of verifiable rules for deterministic, self-expanding reaction classification](https://mlsanity.com/paper/2607.01061/) (2026)
- [Strategy-first synthesis planning for complex natural products](https://mlsanity.com/paper/2608.07454/) (2026)
- [An Agentic Retrobiosynthesis Framework with Learned Frontier Selection](https://mlsanity.com/paper/2608.30702/) (2026)
- [Coverage Before Control: Route-Instruction Grounding and Steering for Controllable Retrosynthesis](https://mlsanity.com/paper/2609.39955/) (2026)

## Abstract (arXiv)

Computer-aided synthesis planning (CASP) has long been envisioned as a complementary tool for synthetic chemists. However, existing frameworks often lack mechanisms to allow interaction with human experts, limiting their ability to integrate chemists' insights. In this work, we introduce Synthelite, a synthesis planning framework that uses large language models (LLMs) to directly propose retrosynthetic transformations. Synthelite can generate end-to-end synthesis routes by harnessing the intrinsic chemical knowledge and reasoning capabilities of LLMs, while allowing expert intervention through natural language prompts. Our experiments demonstrate that Synthelite can flexibly adapt its planning trajectory to diverse user-specified constraints, achieving up to 95% success rates in both strategy-constrained and starting-material-constrained synthesis tasks. Additionally, Synthelite exhibits the ability to account for chemical feasibility during route design. We envision Synthelite to be both a useful tool and a step toward a paradigm where LLMs are the central orchestrators of synthesis planning.