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Open AccessArticle

Combining a QSAR Approach and Structural Analysis to Derive an SAR Map of Lyn Kinase Inhibition

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AgroBioSciences Research Division, Mohammed VI Polytechnic University, Lot 660–Hay Moulay Rachid, 43150 Ben-Guerir, Morocco
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Organic Synthesis, Extraction and Valorization Laboratory, Faculty of Sciences Ain Chock, Hassan II University, Km 8 El Jadida Road, 20100 Casablanca, Morocco
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Team of Molecular Modeling and Spectroscopy, Faculty of Sciences, Chouaib Doukkali University, 24000 El Jadida, Morocco
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School of Agriculture, Fertilizer and Environment Sciences, Mohammed VI Polytechnic University, Lot 660 Hay Moulay Rachid, 43150 Ben Guerir, Morocco
*
Author to whom correspondence should be addressed.
Molecules 2018, 23(12), 3271; https://doi.org/10.3390/molecules23123271
Received: 8 October 2018 / Revised: 15 November 2018 / Accepted: 22 November 2018 / Published: 11 December 2018
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
Lyn kinase, a member of the Src family of protein tyrosine kinases, is mainly expressed by various hematopoietic cells, neural and adipose tissues. Abnormal Lyn kinase regulation causes various diseases such as cancers. Thus, Lyn represents, a potential target to develop new antitumor drugs. In the present study, using 176 molecules (123 training set molecules and 53 test set molecules) known by their inhibitory activities (IC50) against Lyn kinase, we constructed predictive models by linking their physico-chemical parameters (descriptors) to their biological activity. The models were derived using two different methods: the generalized linear model (GLM) and the artificial neural network (ANN). The ANN Model provided the best prediction precisions with a Square Correlation coefficient R2 = 0.92 and a Root of the Mean Square Error RMSE = 0.29. It was able to extrapolate to the test set successfully (R2 = 0.91 and RMSE = 0.33). In a second step, we have analyzed the used descriptors within the models as well as the structural features of the molecules in the training set. This analysis resulted in a transparent and informative SAR map that can be very useful for medicinal chemists to design new Lyn kinase inhibitors. View Full-Text
Keywords: Lyn kinase; inhibitors; QSAR; ANN; GLM; SAR Lyn kinase; inhibitors; QSAR; ANN; GLM; SAR
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MDPI and ACS Style

Naboulsi, I.; Aboulmouhajir, A.; Kouisni, L.; Bekkaoui, F.; Yasri, A. Combining a QSAR Approach and Structural Analysis to Derive an SAR Map of Lyn Kinase Inhibition. Molecules 2018, 23, 3271.

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