Developing therapeutics is a lengthy and expensive process that requires the
satisfaction of many different criteria, and AI models capable of expediting
the process would be invaluable. However, the majority of current AI approaches
address only a narrowly defined set of tasks, often circumscribed within a
particular domain. To bridge this gap, we introduce Tx-LLM, a generalist large
language model (LLM) fine-tuned from PaLM-2 which encodes knowledge about
diverse therapeutic modalities. Tx-LLM is trained using a collection of 709
datasets that target 66 tasks spanning various stages of the drug discovery
pipeline. Using a single set of weights, Tx-LLM simultaneously processes a wide
variety of chemical or biological entities(small molecules, proteins, nucleic
acids, cell lines, diseases) interleaved with free-text, allowing it to predict
a broad range of associated properties, achieving competitive with
state-of-the-art (SOTA) performance on 43 out of 66 tasks and exceeding SOTA on
22. Among these, Tx-LLM is particularly powerful and exceeds best-in-class
performance on average for tasks combining molecular SMILES representations
with text such as cell line names or disease names, likely due to context
learned during pretraining. We observe evidence of positive transfer between
tasks with diverse drug types (e.g.,tasks involving small molecules and tasks
involving proteins), and we study the impact of model size, domain finetuning,
and prompting strategies on performance. We believe Tx-LLM represents an
important step towards LLMs encoding biochemical knowledge and could have a
future role as an end-to-end tool across the drug discovery development
pipeline.

开发疗法是一个漫长而昂贵的过程，需要满足许多不同的标准，而能够加快这个过程的人工智能模型将是无价之宝。这篇论文介绍了 Tx-LLM，它是一个通用的大型语言模型，从 PaLM-2 中细调，能够编码关于不同治疗模式的知识，并在药物发现领域的多个阶段上同时进行预测，从而具有竞争力的性能。我们相信 Tx-LLM 是向编码生化知识的 LLMs 迈出的重要一步，未来可能成为整个药物发现开发过程中的一种端到端工具。