分子发现中的语言模型

摘要

语言模型的成功，尤其是基于Transformer架构的模型，已经渗透到其他领域，催生了在小分子、蛋白质或聚合物上运行的“科学语言模型”。在化学领域，语言模型有助于加速分子发现周期，正如最近在早期药物发现领域取得的有希望的发现所证明的那样。在这里，我们回顾了语言模型在分子发现中的作用，强调了它们在全新药物设计、性质预测和反应化学方面的优势。我们重点介绍了有价值的开源软件资产，从而降低了科学语言建模领域的准入门槛。最后，我们勾勒了一个未来分子设计的愿景，结合了与计算化学工具的访问权限的聊天机器人界面。我们的贡献为对如何利用语言模型加速化学发现感兴趣的研究人员、化学家和人工智能爱好者提供了宝贵资源。

English

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.

分子发现中的语言模型

Language models in molecular discovery

摘要

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