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Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices: A Powerful Tool for Speed up the Discovery of Leads

Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices: A Powerful Tool for Speed up the Discovery of Leads

Gerardo M. Casañola-Martín, Mahmud Tareq Hassan Khan, Huong Le-Thi-Thu, Yovani Marrero-Ponce, Ramón García-Domenech, Francisco Torrens, Antonio Rescigno
Copyright: © 2012 |Volume: 2 |Issue: 2 |Pages: 103
ISSN: 2155-4110|EISSN: 2155-4129|EISBN13: 9781466611122|DOI: 10.4018/ijcce.2012070104
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MLA

Casañola-Martín, Gerardo M., et al. "Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices: A Powerful Tool for Speed up the Discovery of Leads." IJCCE vol.2, no.2 2012: pp.42-144. http://doi.org/10.4018/ijcce.2012070104

APA

Casañola-Martín, G. M., Khan, M. T., Le-Thi-Thu, H., Marrero-Ponce, Y., García-Domenech, R., Torrens, F., & Rescigno, A. (2012). Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices: A Powerful Tool for Speed up the Discovery of Leads. International Journal of Chemoinformatics and Chemical Engineering (IJCCE), 2(2), 42-144. http://doi.org/10.4018/ijcce.2012070104

Chicago

Casañola-Martín, Gerardo M., et al. "Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices: A Powerful Tool for Speed up the Discovery of Leads," International Journal of Chemoinformatics and Chemical Engineering (IJCCE) 2, no.2: 42-144. http://doi.org/10.4018/ijcce.2012070104

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Abstract

In this paper, the authors present an effort to increase the applicability domain (AD) by means of retraining models using a database of 701 great dissimilar molecules presenting anti-tyrosinase activity and 728 drugs with other uses. Atom-based linear indices and best subset linear discriminant analysis (LDA) were used to develop individual classification models. Eighteen individual classification-based QSAR models for the tyrosinase inhibitory activity were obtained with global accuracy varying from 88.15-91.60% in the training set and values of Matthews correlation coefficients (C) varying from 0.76-0.82. The external validation set shows globally classifications above 85.99% and 0.72 for C. All individual models were validated and fulfilled by OECD principles. A brief analysis of AD for the training set of 478 compounds and the new active compounds included in the re-training was carried out. Various assembled multiclassifier systems contained eighteen models using different selection criterions were obtained, which provide possibility of select the best strategy for particular problem. The various assembled multiclassifier systems also estimated the potency of active identified compounds. Eighteen validated potency models by OECD principles were used.

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