Graduate School of Medicine, Kyoto University.
Chem Pharm Bull (Tokyo). 2023;71(6):398-405. doi: 10.1248/cpb.c22-00638.
Drug discovery is researched and developed through many processes, but its overall success rate is extremely low, requiring a very long period of development and considerable costs. Clearly, there is a need to reduce research and development costs by improving the probability of success and increasing process efficiency. One promising approach to this challenge is so-called "in silico drug discovery," which is drug discovery utilizing information and communications technologies (ICT) such as artificial intelligence (AI) and molecular simulation. In recent years, ICT-based science and technology, such as bioinformatics, systems biology, cheminformatics, and molecular simulation, which have been developed mainly in the life science and chemistry fields, have changed the face of drug development. AI-based methods have been developed in the drug discovery process, mainly in relation to drug target discovery and pharmacokinetic analysis. In drug target discovery, an in silico method has been developed that uses a probabilistic framework that eliminates the problems of conventional experimental approaches and provides a key to understanding the pathways and mechanisms from compounds to phenotypes. In the field of pharmacokinetic analysis, we have seen the development of a method using nonclinical data to predict human pharmacokinetic parameters, which are important for predicting drug efficacy and toxicity in clinical trials. In this article, we provide an overview of these methods.
药物发现是通过许多过程研究和开发的，但总体成功率极低，需要非常长的开发时间和相当大的成本。显然，需要通过提高成功率和提高过程效率来降低研究和开发成本。解决这一挑战的一种有前途的方法是所谓的“计算药物发现”，这是利用人工智能（AI）和分子模拟等信息和通信技术（ICT）进行药物发现。近年来，基于信息通信技术的科学技术，如生物信息学、系统生物学、化学信息学和分子模拟，主要在生命科学和化学领域得到发展，改变了药物开发的面貌。在药物发现过程中已经开发了基于人工智能的方法，主要与药物靶标发现和药代动力学分析有关。在药物靶标发现中，已经开发了一种使用概率框架的计算方法，该方法消除了传统实验方法的问题，并为从化合物到表型的途径和机制提供了理解的关键。在药代动力学分析领域，我们已经看到了一种使用非临床数据预测人体药代动力学参数的方法的发展，这对于预测临床试验中的药物疗效和毒性非常重要。在本文中，我们将对这些方法进行概述。
