基于改进人工免疫系统算法的多智能体药物特性预测本体模型。

Q1 Mathematics
Galina Samigulina, Zarina Samigulina
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引用次数: 2

摘要

背景:当前,由于信息技术和计算机设备领域的巨大进步,利用现代人工智能方法来处理大量化学信息以创造具有所需性能的新药是很重要的。跨学科的研究给创造新药带来了额外的困难。目前,还没有通用的算法和软件来预测药物化合物的“结构-性质”依赖性,从而可以考虑到该领域专家的需求。在这方面,基于人工免疫系统的生物启发方法来预测药物化合物的结构-性质依赖性的现代智能系统的发展是相关的。这项工作的目的是开发一个多智能体智能系统,以磺胺类药物化合物为例,使用本体论方法和人工免疫系统的改进算法来预测药物化合物的“结构-性质”依赖性。该系统可以提高“结构-性质”依赖预测模型的准确性,减少获得候选药物化合物的时间和经济成本。方法:在智能系统的创建过程中,使用了多智能体和本体方法,允许结构化输入和输出数据,以最佳方式分配计算资源并协调系统的工作。作为处理大量化学信息、提取信息描述符和创建最佳数据集以及进一步预测药物化合物性质的一种有前途的方法,人们考虑了人工免疫系统的改进算法和各种人工智能算法。结果:建立了多智能体智能系统的本体模型。给出了基于改进的灰狼优化算法和人工免疫系统的“结构-性能”依赖仿真结果。在模拟过程中,使用了来自mol -本能磺胺描述符数据库的信息。结论:所开发的多智能体智能系统采用本体模型,可以直观地呈现智能体功能的结构和相互关系,大大方便了软件的开发,减少了新药开发过程中的时间和财务成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

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来源期刊
Theoretical Biology and Medical Modelling
Theoretical Biology and Medical Modelling MATHEMATICAL & COMPUTATIONAL BIOLOGY-
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Theoretical Biology and Medical Modelling is an open access peer-reviewed journal adopting a broad definition of "biology" and focusing on theoretical ideas and models associated with developments in biology and medicine. Mathematicians, biologists and clinicians of various specialisms, philosophers and historians of science are all contributing to the emergence of novel concepts in an age of systems biology, bioinformatics and computer modelling. This is the field in which Theoretical Biology and Medical Modelling operates. We welcome submissions that are technically sound and offering either improved understanding in biology and medicine or progress in theory or method.
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