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人工智能算法在全新药物结构设计中的应用进展
引用本文:胡建星, 徐旻, 陈红明, 张佩宇, 马健. 人工智能算法在全新药物结构设计中的应用进展[J]. 药学进展, 2021, 45(7): 484-493.
作者姓名:胡建星  徐旻  陈红明  张佩宇  马健
作者单位:1.1. 深圳晶泰科技有限公司, 广东 深圳 518000
摘    要:
人工智能算法在药物设计中得到了越来越广泛的重视,并取得了卓著的研究成果。分子生成算法作为其中一类独特的应用技术,已开始显现出能够取代传统药化专家设计的潜力。为了更好地解决药物发现中的实际问题,精确的分子性质预测模型、三维结构生成和统一的测试数据集都是非常必要的。总结了人工智能算法在全新药物结构设计中的应用进展,重点介绍了不同的分子表征形式、神经网络架构的技术细节及优缺点等。

关 键 词:人工智能  深度学习  分子生成  表征学习  先导化合物优化
收稿时间:2021-06-13

Advances in the Application of Artificial Intelligence Algorithms in the Design of Novel Drug Structures
HU Jianxing, XU Min, CHEN Hongming, ZHANG Peiyu, MA Jian. Advances in the Application of Artificial Intelligence Algorithms in the Design of Novel Drug Structures[J]. Progress in Pharmaceutical Sciences, 2021, 45(7): 484-493.
Authors:HU Jianxing  XU Min  CHEN Hongming  ZHANG Peiyu  MA Jian
Affiliation:1.1. XtalPi Inc., Shenzhen 518000, China
Abstract:
Artificial intelligence (AI) algorithm has attracted an increasing amount of attention and achieved some amazing progress in drug design. Molecular generation as a unique applied technique demonstrates the potential to replace traditional drug design by medicinal chemists. To better utilize it in solving real problems, it is essential to have an accurate molecular property prediction model, a 3D structure generation technique and a unified benchmark. This review summarizes recent advances in the application of AI algorithms in the design of novel drug structures, with particular focus on the technical details as well as the advantages and disadvantages of different molecular representation forms and neural network architectures.
Keywords:artificial intelligence  deep learning  molecular generation  representation learning  lead optimization
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