Intelligencia Inc., New York, New York.
Molecular Carcinogenesis Group, Department of Histology and Embryology, Faculty of Medicine, School of Health Sciences, National Kapodistrian University of Athens, Athens, Greece.
Cancer Res. 2021 Feb 15;81(4):816-819. doi: 10.1158/0008-5472.CAN-20-0866. Epub 2020 Dec 22.
Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard given the complexities of drug biology and clinical trials. This inherent risk is often misunderstood and mischaracterized, leading to inefficient allocation of resources and, as a result, an overall reduction in R&D productivity. Here we argue that the recent resurgence of Machine Learning in combination with the availability of data can provide a more accurate and unbiased estimate of drug development risk.
确定新的药物靶点和开发安全有效的药物既具有挑战性又充满风险。此外，鉴于药物生物学和临床试验的复杂性，药物开发风险（即药物最终获得监管批准的可能性）的特征一直以来都非常困难。由于这种内在风险常常被误解和歪曲，导致资源配置效率低下，从而降低了研发生产力。在这里，我们认为机器学习的最新复兴与数据的可用性相结合，可以为药物开发风险提供更准确和无偏的估计。
