基于深度学习理论的药物先导化合物自主感知及优化策略研究  

作　　者：罗佳 许小青 王炜祺 张威 

机构地区：[1]江苏卫生健康职业学院药学院,南京211800

出　　处：《科技创新与应用》2023年第22期86-89,共4页Technology Innovation and Application

基　　金：江苏高校哲学社会科学研究一般项目(2022SJYB0866);江苏卫生健康职业学院校级科研项目(JKD202002,JKC2021002)。

摘　　要：新药研发产业链具有高度密集的可用数据集,深度学习理论具有强大的密集数据集隐性特征自感知属性,为人工智能在新药研发领域的应用提供先天优势。先导化合物(Lead Compound,LC)是现代新药研究的出发点,先导化合物的发现及结构优化策略具有显著的大数据挖掘特征,利用具有强大感知能力的深度学习理论构建药物先导化合物自主感知及优化策略模型,实现具有某种生物活性和特定化学结构的先导化合物自主感知及优化策略生成,具有显著的经济及社会效益。在深度调研基于人工智能和大数据的精准药物设计技术在国内外的最新研究现状的基础上,从拟解决的关键问题、研究方法、技术路线等方面开展基于深度学习理论的药物先导化合物自主感知及优化策略研究,为人工智能药物研发领域提供系统性研究案例。The new drug research and development industry chain has a highly dense available data set,and the deep learning theory has a strong hidden feature self-perception attribute of the dense data set,which provides an inherent advantage for the application of artificial intelligence in the field of new drug research and development.Lead compound(LC)is the starting point of modern new drug research.The discovery and structure optimization strategy of lead compounds have significant big data mining characteristics.The model of autonomous perception and optimization strategy of drug leading compounds is constructed by using the deep learning theory with strong perception ability,so as to realize the autonomous perception and optimization strategy generation of lead compounds with certain biological activity and specific chemical structure.It has remarkable economic and social benefits.Based on in-depth research on the latest research status of precision drug design technology based on AI and big data at home and abroad,the research on autonomous perception and optimization strategy of drug lead compounds based on deep learning theory was carried out from the key issues to be solved,research methods,technical routes,etc.,thereby provides systematic research cases for the field of artificial intelligence drug research and development.

关 键 词：深度学习理论 药物先导化合物 自主感知 优化策略模型 药物研发 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]