Novartis Institutes for Biomedical Research, 4002, Basel, Switzerland.
Microsoft Research AI4Science, CB1 2FB, Cambridge, UK.
Nat Commun. 2023 Oct 31;14(1):6651. doi: 10.1038/s41467-023-42242-1.
The lead optimization process in drug discovery campaigns is an arduous endeavour where the input of many medicinal chemists is weighed in order to reach a desired molecular property profile. Building the expertise to successfully drive such projects collaboratively is a very time-consuming process that typically spans many years within a chemist's career. In this work we aim to replicate this process by applying artificial intelligence learning-to-rank techniques on feedback that was obtained from 35 chemists at Novartis over the course of several months. We exemplify the usefulness of the learned proxies in routine tasks such as compound prioritization, motif rationalization, and biased de novo drug design. Annotated response data is provided, and developed models and code made available through a permissive open-source license.
在药物发现项目中，先导化合物优化是一项艰巨的工作，需要权衡许多药物化学家的意见，以达到理想的分子性质特征。建立成功协作驱动此类项目的专业知识是一个非常耗时的过程，通常在化学家的职业生涯中需要花费数年时间。在这项工作中，我们旨在通过应用人工智能学习排序技术，对诺华公司的 35 位化学家在几个月的时间内提供的反馈进行复制。我们通过对化合物优先级排序、基序合理化和有偏差的从头药物设计等常规任务中的学习代理的应用示例，说明了它们的有用性。提供了带注释的响应数据，并通过宽松的开源许可证提供了开发的模型和代码。
