Dipartimento di Scienze del Farmaco e della Salute, Università di Catania, Viale A. Doria 6, 95125 Catania, Italy.
Int J Mol Sci. 2022 Mar 17;23(6):3261. doi: 10.3390/ijms23063261.
The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and beyond. Drug development is a costly and time-consuming business, and only a minority of approved drugs generate returns exceeding the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper reviews the most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research.
最近的新冠危机为学术界和工业界提供了有关数字化重组的重要经验教训。这些时期的一个有趣教训是数据分析和人工智能的巨大潜力。这场危机使分析和人工智能的采用呈指数级加速，预计这种势头将持续到 2020 年代及以后。药物开发是一项昂贵且耗时的业务，只有少数获得批准的药物产生的回报超过研发成本。因此，人们迫切希望使药物发现更便宜、更快。有了现代算法和硬件，人工智能等新技术和其他计算模拟工具可以帮助药物开发者，这并不奇怪。在仅仅两年的新冠研究中，许多新型分子已经使用人工智能方法设计/鉴定，其在时间和效果方面取得了惊人的成果。本文综述了人工智能在新冠药物研究中的从头药物设计方面的重要研究。
