Abstract
QSAR (Quantitative Structure-Activity Relationship) modelling is one of the well developed areas in drug development through computational chemistry. This kind of relationship between molecular structure and change in biological activity is center of focus for QSAR modelling. Machine learning algorithms are important tools for QSAR analysis, as a result, they are integrated into the drug production process. In this paper we will try to go through the problem of learning the Complex-Valued Neural Networks(CVNNs) using Particle Swarm Optimization(PSO); which is one of the open topics in the machine learning society. We presents CVNN model for real-valued regression problems. We tested such trained CVNN on two drug sets as a real world benchmark problem. The results show that the prediction and generalization abilities of CVNNs is superior in comparison to the conventional real-valued neural networks (RVNNs). Moreover, convergence of CVNNs is much faster than that of RVNNs in most of the cases.
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References
Lipinski, C.: Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov. Today: Technol. 1(4), 337–341 (2004)
Leeson, P., Davis, A., Steele, J.: Drug-like properties: guiding principles for design or chemical prejudice? Drug Discov. Today: Technol. 1(3), 189–195 (2004)
Li, A.P.: Preclinical in vitro screening assays for drug-like properties. Drug Discov. Today: Technol. 2(2), 179–185 (2005)
Hansch, C.: A quantitative approach to biochemical structure-activity relationships. Acc. Chem. Res. 2, 232–239 (1969)
Duch, W., Swaminathan, K., Meller J.: Artificial Intelligence Approaches for Rational Drug Design and Discovery Current Pharmaceutical Design, 13, (2007)
Chin, L., Chun, Y.: In: Dehmer, M., Varmuza, K., Bonchev, D. (eds.) Current Modelling Methods Used in QSAR/QSPR, in Statistical Modelling of Molecular Descriptors in QSAR/QSPR. Wiley-VCH Verlag GmbH & Co, Weinheim (2012)
Gertrudesa, J., Maltarollob, V., Silvaa, R., Oliveiraa, P., Honrioa, K., da Silva, A.: Machine learning techniques and drug design. Curr. Med. Chem. 19, 4289–4297 (2012)
El-Telbany, M.: The predictive learning role in drug design. J. Emerg. Trends Comput. Inf. Sci. JETCIS 5(3) (2014)
Bishop, C.: Pattern Recognition and Machine Learning, 2nd edn. Springer, Berlin (2006)
Marsland, S.: Machine Learning—An Algorithmic Perspective. Chapman and Hall, CRC, Boca Raton (2009)
Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. MIT Press (2012)
Hirose, A.: Nature of complex number and complex-valued neural networks. Front. Electr. Electron. Eng. China 6, 171–180 (2011)
Kobayashi, M.: Exceptional reducibility of complex-valued neural networks. IEEE Trans. Neural Netw. 21, 1060–1072 (2010)
Hirose, A., Yoshida, S.: Generalization characteristics of complex-valued feedforward neural networks in relation to signal coherence. IEEE Trans. Neural Netw. Learn. Syst. 23, 541–551 (2012)
Agrafiotis, D., Cedeno, W.: Feature selection for structure-activity correlation using binary paricle swarms. J. Med. Chem. 45, 1098 (2002)
Cedeno, W., Agrafiotis, D.: A comparison of particle swarms techniques for the development of quantitative structure-activity relationship models for drug design. In: Proceedings of the IEEE Computational Systems Bioinformatics (2005)
El-Telbany, M., Refat, S., Nasr, E.: Particle swarm optimization for drug design. In: Proceedings of the 1st International Workshop on Mechatronics Education, Taif, Saudi Arabia, pp. 117–122 (2015)
Aizenberg, I., Moraga, C.: Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm. Soft Comput. 11(2), 169–183 (2007)
Kitajima, T., Yasuno, T.: Output Prediction of Wind Power Generation System Using Complex-valued Neural Network Proceedings SICE Annual Conference, pp. 3610–3613 (2010)
Nitta, T.: An extension of the back-propagation algorithm to complex numbers. Neural Netw. 10(8), 1391–1415 (1997)
Ozdemir, N., Iskender, B., Ozgur, N.: Complex valued neural network with Möbius activation function. Commun. Nonlinear Sci. Numer. Simul. 16(12), 4698–4703 (2011)
Kennedy, J., Eberhart, C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Newman, D., Hettich, S., Blake, C., Merz, C.: UCI Repository of Machine Learning Databases. Department of Information and Computer Science, University California, Irvine (1998)
Faijul, A., Murase, K.: Single-layerd complex-valued neural network for real-valued classification problems. Neurocomputing 72, 945–955 (2009)
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El-Telbany, M.E., Refat, S., Nasr, E.I. (2016). A New Learning Strategy for Complex-Valued Neural Networks Using Particle Swarm Optimization. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_18
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