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.

中文翻译：

---

基于人工免疫系统改进算法预测药物特性的多智能体智能系统本体模型。

目前，由于信息技术和计算机设备领域的巨大进步，使用现代人工智能方法来处理广泛的化学信息以创造具有所需特性的新药物非常重要。跨学科的研究给新药的研发带来了额外的困难。目前，还没有能够考虑到该领域专家需求的通用算法和软件来预测药物化合物的“结构-性质”依赖性。在这方面，基于人工免疫系统的有前途的生物启发方法的现代智能系统的开发用于预测药物化合物的结构-性质依赖性是相关的。这项工作的目的是开发一种多智能体智能系统，以磺酰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。所提出的系统可以提高“结构-性质”依赖性预测模型的准确性，从而减少获得候选药物化合物的时间和财务成本。在智能系统的创建过程中，使用了多代理和本体方法，这些方法允许构建输入和输出数据，以最佳方式分配计算资源并协调系统的工作。作为一种处理大量化学信息、提取信息描述符、创建最佳数据集以及进一步预测药物化合物特性的有前途的方法，人们认为人工免疫系统的改进算法和各种算法人工智能。开发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-性质”依赖性模拟的结果。在模拟过程中，使用了来自 Mol-Instincts 磺酰胺描述符数据库的信息。使用本体模型开发的多智能体智能系统可以直观地呈现智能体功能的结构和相互关系，这极大地方便了软件的开发，并减少了新药开发过程中的时间和财务成本。