The ontological models of swarm intelligence algorithms for immune network modeling of drugs
AbstractThe article is devoted to development of information system of forecasting properties of new drugs based on methods of swarm intelligence and artificial immune systems. An In forecasting relationship between the chemical structure of substances and their biological activity (QSAR, Quantitative Structure-Activity Relationship) an important aspect is feature selection. A perspective direction in QSAR is the use of artificial intelligence, which provide high accuracy of forecasting of chemical compounds with predetermined properties. The article considers the methods of bee colonies and particle swarm algorithm for solving the problem of feature selection and further immune network modeling of pharmacological activity of chemical compounds. The existing software of implementation of these algorithms for constructing of optimum set of descriptors are given. Application of multialgorithmic approach in immune network modeling of drugs requires the systematization of using methods and development of an integrated ontological model. Development of ontological models allows to structure input and output data, to consider features of functioning and relationships, saves time and computational resources in the development of component-based software for immune network modeling of new substances with predetermined pharmacological activity. The ontological models of bee colony and particle swarm algorithm for solving the problem of feature selection are created in ontology editor Protege.
 Baranjuk V.V. and Smirnova O.S., "Expanding the bionics ontology by the description of swarm intelligence" International Journal of Open Information Technologies 3(12) (2015): 13-17.
 Chin Y.L. and Chun W.Y., "Current Modeling Methods Used in QSAR/QSPR,"Statistical Modelling of Molecular Descriptors in QSAR/QSPR(2012): 1-31.
 Dasgupta D., Artificial Immune Systems and Their Applications (NY: Springer-Verlag, 1998), 438.
 Gladun A.Ya., Rogushina Yu.V. (2006) Ontologii v korporativnyh sistemah [Ontologies in enterprise systems]. Korporativnye sistemy, vol. 1, accessed December 19, 2016, http://www.management.com.ua/ims/ims116.html.
 Golla S. et al., "Virtual design of chemical penetration enhancers for transdermal drug delivery,"Chem. Biol. Drug Design (2012): 478-487.
 Hamed Z., Aboozar K., Hamid M., "Application of modified particle swarm optimization as an efficient variable selection strategy in QSAR/QSPR studies"Journal of chemometrics (2012): 123-128.
 Oleinik An.A., Oleinik Al.A., Subbotin S.A. (2012) Agentnye texnologii dlya otbora informativnyh priznakov [Agent technologies for the selection of informative features]. Kibernetika i sistemnyi analiz, vol. 2, pp. 113-125.
 Oleinik A.A. (2011) Multiagentnyi metod optimizacii s adaptivnymi parametrami [Multi algorithmic approach with adaptive parameters]. Iskusstvennyi intellekt, vol.1, pp. 83-90.
 Raevskii O.A. (2006) Deskriptory bodorodnoi svyazi v komputernom moleculyarnom dizaine [Descriptors of hydrogen bond in computer molecular design]. Ros. xim. jurnal, vol. 2, pp. 97-108.
 Rangel C. et al., "An Approach for the Emerging Ontology Alignment based on the Bees Colonies,"Int’l Conf. Artificial Intelligence (2015): 536-541.
 Samigulina G.A. and Samigulina Z.I., "Computational Molecular Design of Antiseptic Drags based on Immune Network Modeling,"Proceedings of the 12-th International Conference on Electronics Computer and Computation "ICECCO- 2015" (Almaty: Suleyman Demirel University, 2015): 47-52.
 Samigulina G.A. and Samigulina Z.I. "Drag Design of sulfanilamide based on Immune Network Modeling and Ontological approach,"Proc. of the 10th IEEE Int. Conf. on Application of Information and Communication Technologies AICT2016, accessed January 19, 2017, www.aict.info/2016.
 Samigulina G.A. et al. (2015)Komputernyi molekulyarnyi dizain lekarstvennyh preparatov na osnove immunnosetenovogo modelirovaniya [Computer-aided molecular design of drugs based on immune network modeling].Otchet o NIR, pp. 145.
 Samigulina G.A., Samigulina Z.I. (2013) Postroenie optimalnoi immunnosetevoi modeli dlya prognozirovaniya svoistv neizvestnyh lekarstvennyh soedinenii na osnove multialgoritmicheskogo podhoda [Construction of an optimal immune-network model for predicting the properties of unknown drug compounds on the basis of a multi-algorithmic approach]. Problemy informatiki, pp. 21-29.
 Schiezaro M. and Pedrini H., "Data feature selection based on Artificial bee colony algorithm," EURASIP Journal on Image and Video processing(2013): 29-33.
 Shao L. et al., "Particle Swarm Optimization Algorithm Based on Semantic Relations and Its Engineering Applications,"Systems Engineering Procedia (2012): 222-227.
 Shi-xiong X. et al., "Fault Diagnosis Method Based on Ontology and Particle Swarm-Immune Optimization Algorithm in the Motor,"Multimedia and Signal Processing (CMSP)(2011): 1-9.
 Tarakanov A. and Nicosia G., "Foundations of immunocomputing,"Proceedings of the 1-st IEEE Symposium of Computational Intelligence (Honolulu, 2007): 503-508.
 Xodashinskii I.A., Gorbunov I.V., Dudin P.A., Sinkov D.S. (2012) Postroenie nechetkih sistem prognozirovaniya effektivnosti nemedikamentoznogo lecheniya [Construction of fuzzy systems for predicting the effectiveness of non-drug treatment]. Intellektualnye systemy, vol. 3, no 33, pp. 140-149.
 Yuan F. et al., "Artificial bee colony-based extraction of non-taxonomic relation between symptom and syndrome in TCM records,"International Journal of Computing Science and Mathematics(2015), accessed January 7, 2017, DOI: http://dx.doi.org/10.1504/IJCSM.2015.073600.