National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; ApconiX, BioHub at Alderley Park, Alderley Edge SK10 4TG, UK; University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
Drug Discov Today. 2021 Nov;26(11):2593-2607. doi: 10.1016/j.drudis.2021.06.009. Epub 2021 Jun 30.
The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artificial intelligence (AI), language models (LMs) have changed the landscape of natural language processing (NLP), offering possibilities to transform treatment development more effectively. Here, we summarize advances in AI-powered LMs and their potential to aid drug discovery and development. We highlight opportunities for AI-powered LMs in target identification, clinical design, regulatory decision-making, and pharmacovigilance. We specifically emphasize the potential role of AI-powered LMs for developing new treatments for Coronavirus 2019 (COVID-19) strategies, including drug repurposing, which can be extrapolated to other infectious diseases that have the potential to cause pandemics. Finally, we set out the remaining challenges and propose possible solutions for improvement.
新药的发现和开发成本高昂、耗时且效率往往不高，在此过程中往往会遭遇许多失败。人工智能（AI）驱动的语言模型（LMs）改变了自然语言处理（NLP）的格局，为更有效地进行治疗开发提供了可能性。在这里，我们总结了人工智能驱动的 LMs 的进展及其在药物发现和开发中的潜在应用。我们强调了人工智能驱动的 LMs 在目标识别、临床设计、监管决策和药物警戒方面的机会。我们特别强调人工智能驱动的 LMs 在开发针对 2019 年冠状病毒（COVID-19）的新治疗策略方面的潜在作用，包括药物重定位，这可以推广到其他具有潜在大流行风险的传染病。最后，我们列出了剩余的挑战，并提出了可能的改进解决方案。
