• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Theoretical Biology and Medical Modelling >Ontological model of multi-agent Smart-system for predicting drug properties based on modified algorithms of artificial immune systems
【24h】

Ontological model of multi-agent Smart-system for predicting drug properties based on modified algorithms of artificial immune systems

机译：基于人工免疫系统改性算法预测药物特性的多剂智能系统的本体论模型

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Currently, due to the huge progress in the field of information technologies and computer equipment, it is important to use modern approaches of artificial intelligence in order to process extensive chemical information at creating new drugs with desired properties. The interdisciplinary of research creates additional difficulties in creating new drugs. Currently, there are no universal algorithms and software for predicting the “structure-property” dependence of drug compounds that can take into account the needs of specialists in this field. In this regard, the development of a modern Smart-system based on the promising bio-inspired approach of artificial immune systems for predicting the structure-property dependence of drug compounds is relevant. The aim of this work is to develop a multi-agent Smart-system for predicting the “structure-property” dependence of drug compounds using the ontological approach and modified algorithms of artificial immune systems using the example of drug compounds of the sulfonamide group. The proposed system makes it possible to increase the accuracy of prediction models of the “structure-property” dependence, to reduce the time and financial costs for obtaining candidate drug compounds. During the creation of a Smart-system, there are used multi-agent and ontological approaches, which allow to structure input and output data, optimally to distribute computing resources and to coordinate the work of the system. As a promising approach for processing a large amount of chemical information, extracting informative descriptors and for the creation of an optimal data set, as well as further predicting the properties of medicinal compounds, there are considered modified algorithms of artificial immune systems and various algorithms of artificial intelligence. There was developed an ontological model of a multi-agent Smart-system. There are presented the results of the ?structure-property? dependence simulation based on a modified grey wolf optimization algorithm and artificial immune systems. During the simulation, there was used information from the Mol-Instincts sulfonamide descriptor database. The developed multi-agent Smart-system using ontological models allows visually to present the structure and interrelationships of agents functioning, which greatly facilitates the development of software and reduces time and financial costs during the development of new drugs.
机译：目前，由于信息技术和计算机设备领域的巨大进展，重要的是利用现代人工智能方法，以便在创造具有所需特性的新药物时处理广泛的化学信息。研究的跨学科创造了创造新药的额外困难。目前，没有通用算法和软件，用于预测药物化合物的“结构性”依赖性，这些化合物可以考虑到该领域的专家需求。在这方面，基于有前途的生物启发方法的人工免疫系统方法的现代智能系统的发展有所相关。这项工作的目的是开发一种多种子体智能系统，用于使用本发明的人工免疫系统的本体化学方法和使用磺酰胺基团的实例来预测药物化合物的“结构性”依赖性。所提出的系统使得可以提高“结构性质”依赖性的预测模型的准确性，以减少获得候选药物化合物的时间和财务成本。在创建智能系统期间，使用多种代理和本体方法，这允许构建输入和输出数据，最佳地分配计算资源并协调系统的工作。作为加工大量化学信息的有希望的方法，提取信息描述符和用于创建最佳数据集，以及进一步预测药物化合物的性质，考虑了人工免疫系统和各种算法的修改算法和各种算法人工智能。开发了一种多功能智能系统的本体模型。介绍了这个结构性的结果吗？基于改进的灰狼优化算法和人工免疫系统的依赖性仿真。在模拟期间，使用来自摩洛胺描述符数据库的Mol-Instincts的信息。使用本体模型的开发的多代理智能系统允许目视呈现出色的结构和相互关系，这极大地促进了软件的发展，并减少了新药开发期间的时间和金融成本。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号