Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road CB2 1EW, UK; Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK.
European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK.
Drug Discov Today. 2021 Feb;26(2):511-524. doi: 10.1016/j.drudis.2020.12.009. Epub 2020 Dec 17.
Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. This article quantifies the stages of drug discovery in which improvements in the time taken, success rate or affordability will have the most profound overall impact on bringing new drugs to market. Changes in clinical success rates will have the most profound impact on improving success in drug discovery; in other words, the quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost. Although current advances in AI focus on how to make a given compound, the question of which compound to make, using clinical efficacy and safety-related end points, has received significantly less attention. As a consequence, current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo. Thus, addressing the questions of which data to generate and which end points to model will be key to improving clinically relevant decision-making in the future.
虽然人工智能（AI）在图像识别等领域产生了深远的影响，但在药物发现方面取得类似的进展却很少见。本文对药物发现的各个阶段进行了量化，这些阶段在耗时、成功率或可负担性方面的改进将对新药推向市场产生最深远的整体影响。临床成功率的变化将对提高药物发现的成功率产生最深远的影响；换句话说，决定推进哪个化合物（以及如何进行临床试验）的决策质量比速度或成本更为重要。尽管当前人工智能的进展集中在如何制造给定的化合物，但使用与临床疗效和安全性相关的终点来制造哪种化合物的问题却受到了明显较少的关注。因此，当前的替代指标和可用数据无法充分利用人工智能在药物发现中的潜力，尤其是在涉及体内药物疗效和安全性方面。因此，解决生成哪些数据和建模哪些终点的问题将是未来提高临床相关决策的关键。
