分子發現中的語言模型

Language models in molecular discovery

September 28, 2023

作者: Nikita Janakarajan, Tim Erdmann, Sarath Swaminathan, Teodoro Laino, Jannis Born

cs.AI

摘要

語言模型的成功，特別是基於Transformer的架構，已經滲透到其他領域，催生了在小分子、蛋白質或聚合物上運作的「科學語言模型」。在化學領域，語言模型有助於加速分子發現週期，正如最近在早期藥物發現領域取得的有希望的發現所證實的。在這裡，我們回顧了語言模型在分子發現中的作用，強調它們在全新藥物設計、性質預測和反應化學中的優勢。我們突出了有價值的開源軟體資產，從而降低了進入科學語言建模領域的門檻。最後，我們描繪了一個將聊天機器人界面與計算化學工具結合的未來分子設計展望。我們的貢獻為對想要了解語言模型如何以及將如何用於加速化學發現的研究人員、化學家和人工智慧愛好者提供了寶貴資源。

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.