Multi-agent System for Forecasting Based on Modified Algorithms of Swarm Intelligence and Immune Network Modeling

Samigulina, Galina; Massimkanova, Zhazira

doi:10.1007/978-3-319-92031-3_19

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Currently, there are described various specific mechanisms of immunity functioning. For example, the immune network modeling algorithm [ 35 ] is based on molecular recognition between formal peptides, etc. There are several main directions of AIS based on clonal selection [ 36 ], immune network modeling (INM) and negative selection (NS) [ 37 ].…”

Section: Methods and Algorithms Of Researchmentioning

confidence: 99%

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

Samigulina

2020

Theor Biol Med Model

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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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Section: Methods and Algorithms Of Researchmentioning

confidence: 99%