# BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction

arXiv: https://arxiv.org/abs/2408.10285 · HTML: https://mlsanity.com/paper/2408.10285/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [CMMLU: Measuring massive multitask language understanding in Chinese](https://mlsanity.com/paper/2306.09212/) (2023)
- [What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks](https://mlsanity.com/paper/2305.18365/) (2023)
- [Retrosynthesis Prediction with Conditional Graph Logic Network](https://mlsanity.com/paper/2001.01408/) (2020)
- [Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective](https://mlsanity.com/paper/2306.06615/) (2023)
- [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)
- [RetroXpert: Decompose Retrosynthesis Prediction like a Chemist](https://mlsanity.com/paper/2011.02893/) (2020)
- [Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction](https://mlsanity.com/paper/2110.09681/) (2021)
- [Learning Graph Models for Retrosynthesis Prediction](https://mlsanity.com/paper/2006.07038/) (2020)
- [Developing ChemDFM as a large language foundation model for chemistry](https://mlsanity.com/paper/2401.14818/) (2024)
- [Can Large Language Models Empower Molecular Property Prediction?](https://mlsanity.com/paper/2307.07443/) (2023)
- [BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer](https://mlsanity.com/paper/2307.00360/) (2023)
- [Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees](https://mlsanity.com/paper/1908.10410/) (2019)

## Built on by (most cited first, 9 of 9)

- [Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning](https://mlsanity.com/paper/2502.02871/) (2025)
- [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)
- [ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models](https://mlsanity.com/paper/2505.12534/) (2025)
- [SpindleKV: A Novel KV Cache Reduction Method Balancing Both Shallow and Deep Layers](https://mlsanity.com/paper/2507.06517/) (2025)
- [Large Language Model Agent for User-friendly Chemical Process Simulations](https://mlsanity.com/paper/2601.11650/) (2026)
- [Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration](https://mlsanity.com/paper/2510.16590/) (2025)
- [CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models](https://mlsanity.com/paper/2508.17243/) (2025)
- [RTMol: Rethinking Molecule-text Alignment in a Round-trip View](https://mlsanity.com/paper/2511.12135/) (2025)

## Abstract (arXiv)

Retrosynthesis analysis is pivotal yet challenging in drug discovery and organic chemistry. Despite the proliferation of computational tools over the past decade, AI-based systems often fall short in generalizing across diverse reaction types and exploring alternative synthetic pathways. This paper presents BatGPT-Chem, a large language model with 15 billion parameters, tailored for enhanced retrosynthesis prediction. Integrating chemical tasks via a unified framework of natural language and SMILES notation, this approach synthesizes extensive instructional data from an expansive chemical database. Employing both autoregressive and bidirectional training techniques across over one hundred million instances, BatGPT-Chem captures a broad spectrum of chemical knowledge, enabling precise prediction of reaction conditions and exhibiting strong zero-shot capabilities. Superior to existing AI methods, our model demonstrates significant advancements in generating effective strategies for complex molecules, as validated by stringent benchmark tests. BatGPT-Chem not only boosts the efficiency and creativity of retrosynthetic analysis but also establishes a new standard for computational tools in synthetic design. This development empowers chemists to adeptly address the synthesis of novel compounds, potentially expediting the innovation cycle in drug manufacturing and materials science. We release our trial platform at urlhttps://www.batgpt.net/dapp/chem.