The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to "scientific language models" that operate on small molecules, proteins or polymers. In chemistry, language models contribute to accelerating the molecule discovery cycle as evidenced by promising recent findings in early-stage drug discovery. Here, we review the role of language models in molecular discovery, underlining their strength in de novo drug design, property prediction and reaction chemistry. We highlight valuable open-source software assets thus lowering the entry barrier to the field of scientific language modeling. Last, we sketch a vision for future molecular design that combines a chatbot interface with access to computational chemistry tools. Our contribution serves as a valuable resource for researchers, chemists, and AI enthusiasts interested in understanding how language models can and will be used to accelerate chemical discovery.

翻译：语言模型（尤其是基于Transformer架构）的成功已渗透至其他领域，催生了作用于小分子、蛋白质或聚合物的"科学语言模型"。在化学领域，早期药物发现中令人瞩目的最新成果表明，语言模型能加速分子发现周期。本文综述了语言模型在分子发现中的作用，重点阐述了其在新药从头设计、性质预测及反应化学中的优势。我们着重介绍了宝贵的开源软件资源，从而降低了科学语言建模领域的准入门槛。最后，我们展望了结合聊天机器人界面与计算化学工具的分子设计未来愿景。本文将为关注语言模型如何加速化学发现的研究人员、化学家及人工智能爱好者提供重要参考。