Novartis Institutes for BioMedical Research, Cambridge, Massachusetts 02139, United States.
Novartis Institutes for BioMedical Research, Emeryville, California 94608, United States.
J Med Chem. 2020 Aug 27;63(16):8824-8834. doi: 10.1021/acs.jmedchem.9b02130. Epub 2020 Mar 30.
Artificial intelligence (AI) is becoming established in drug discovery. For example, many in the industry are applying machine learning approaches to target discovery or to optimize compound synthesis. While our organization is certainly applying these sorts of approaches, we propose an additional approach: using AI to augment human intelligence. We have been working on a series of recommendation systems that take advantage of our existing laboratory processes, both wet and computational, in order to provide inspiration to our chemists, suggest next steps in their work, and automate existing workflows. We will describe five such systems in various stages of deployment within the Novartis Institutes for BioMedical Research. While each of these systems addresses different stages of the discovery pipeline, all of them share three common features: a trigger that initiates the recommendation, an analysis that leverages our existing systems with AI, and the delivery of a recommendation. The goal of all of these systems is to inspire and accelerate the drug discovery process.
人工智能(AI)在药物发现中已经得到了广泛应用。例如,许多业内人士正在将机器学习方法应用于靶点发现或化合物合成优化。虽然我们组织肯定也在应用这些方法,但我们提出了另一种方法:利用人工智能来增强人类的智力。我们一直在研究一系列推荐系统,这些系统利用我们现有的实验室流程(包括湿实验和计算实验),为我们的化学家提供灵感,为他们的工作建议下一步,并自动化现有的工作流程。我们将描述诺华生物医学研究所(Novartis Institutes for BioMedical Research)内正在部署的五个此类系统。虽然这些系统中的每一个都针对发现管道的不同阶段,但它们都具有三个共同的特点:触发推荐的触发器,利用我们现有的 AI 系统进行分析,以及推荐的生成。所有这些系统的目标都是激发和加速药物发现过程。