基于人工智能的药物研发:目前的进展和未来的挑战  

Artificial intelligence-based drug development:current progress and future challenges

作　　者：余泽浩 张雷明 张梦娜 戴智琦 彭成斌 郑四鸣 YU Zehao;ZHANG Leiming;ZHANG Mengna;DAI Zhiqi;PENG Chengbin;ZHENG Siming(Health Science Center,Ningbo University,Ningbo 315211;The First Affiliated Hospital of Ningbo University,Ningbo 315000;College of Information Science and Engineering,Ningbo University,Ningbo 315211,China)

机构地区：[1]宁波大学医学部,宁波315211 [2]宁波大学附属第一医院,宁波315000 [3]宁波大学信息科学与工程学院,宁波315211

出　　处：《中国药科大学学报》2023年第3期282-293,共12页Journal of China Pharmaceutical University

基　　金：浙江省中医药科技计划资助项目(No.2022ZB323);浙江省医药卫生科技计划资助项目(No.2022KY1114);宁波市自然科学基金资助项目(No.2021J268)。

摘　　要：近年来,人工智能在药物研发领域得到了广泛的应用。特别是自然语言处理技术在预训练模型的出现后有了非常显著的提高,在此基础上,图神经网络的引入也使得药物研发变得更加准确和高效。为了使药物研发者更加系统全面地了解人工智能在药物研发中的应用,本文介绍了人工智能中的前沿算法,同时阐述了人工智能在药物小分子设计、虚拟筛选、药物再利用以及药物性质预测等多方面的应用场景,最后探讨它在未来药物研发中的机遇与挑战。In recent years,artificial intelligence(AI)has been widely applied in the field of drug discovery and development.In particular,natural language processing technology has been significantly improved after the emergence of the pre-training model.On this basis,the introduction of graph neural network has also made drug development more accurate and efficient.In order to help drug developers more systematically and comprehensive⁃ly understand the application of artificial intelligence in drug discovery,this article introduces cutting-edge algo⁃rithms in AI,and elaborates on the various applications of AI in drug development,including drug small mole⁃cule design,virtual screening,drug repurposing,and drug property prediction,finally discusses the opportunities and challenges of AI in future drug development.

关 键 词：人工智能 专家系统 机器学习 自然语言处理 图神经网络 药物设计 药物研发 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] R914[自动化与计算机技术—控制科学与工程]