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 \url{https://www.batgpt.net/dapp/chem}.

翻译：逆合成分析在药物发现与有机化学中至关重要且极具挑战性。尽管过去十年间计算工具激增，但基于人工智能的系统在泛化至不同反应类型以及探索替代合成路径方面仍存在不足。本文提出BatGPT-Chem，这是一个拥有150亿参数的大型语言模型，专为增强逆合成预测而设计。该方法通过自然语言与SMILES符号的统一框架整合化学任务，并从庞大的化学数据库中综合生成大量指令数据。通过在超过一亿个实例上采用自回归与双向训练技术，BatGPT-Chem捕获了广泛的化学知识，能够精确预测反应条件，并展现出强大的零样本能力。我们的模型在生成复杂分子的有效策略方面优于现有人工智能方法，这已通过严格的基准测试验证，标志着显著进步。BatGPT-Chem不仅提升了逆合成分析的效率与创造性，还为合成设计中的计算工具设立了新标准。这一进展使化学家能够熟练应对新型化合物的合成，有望加速药物制造与材料科学的创新周期。我们的试用平台发布于 \url{https://www.batgpt.net/dapp/chem}。