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[] Ligand-based virtual screening of a benzylisoquinoline alkaloids dataset with anti-inflammatory potential activity.

1 Federal University of Paraíba, Campus I, João Pessoa-PB, Brazil
2 Hospital Universitário - Universidade Federal da Paraíba
* Author to whom correspondence should be addressed.
11 January 2017
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Abstract

Inhibitor of nuclear factor kappa B kinase beta subunit (IKK-B) and extracellular signal-regulated kinase 1 (IKK-B) are two proteins involved in cytokine intracellular signaling pathways, which have a great importance due to their anti-inflammatory role. In this work, from the ChemBL database were obtained 775 and 48 structures with activity against IKK-B (CHEMBL1991) and ERK1 (CHEMBL3385) respectively. The compounds were classified using values of pIC50, presenting a range of 4.29 (from 5.01 to 9.30) for IKK-B and 3.10 (From 5.05 to 8.15) for ERK-1. From SMILES codes, 2D structures were generated in Standardizer and after calculated 1064 two-dimensional molecular descriptors in Dragon 7 software. Obtained results were imported to Knime 3.1.0 software. All variables were submitted to autoscaling and after were partitioned to generate two groups, a training group composed by the 80% of the whole molecules set and a test group composed by the remaining 20%. (Q)SAR models was performed using a Random Forest algorithm.  Models were evaluated through cross validation (leave-one-out), Q 2LOO = 0.69 and 0.66 as well as external test, Q 2ext = 0.74 and 0.58 for IKK-B and ERK1 respectively. Finally, pIC50 value of 179 benzylisoquinoline alkaloids were predicted in the (Q)SAR models found 4 compound with the highest activity for each one protein studied.

Keywords

Ligand-based virtual screening; benzylisoquinoline alkaloids, anti-inflammatory

Cite this article as

Acevedo, C.; Scotti, L.; Alves, M.; Diniz, M.; Scotti, M. Ligand-based virtual screening of a benzylisoquinoline alkaloids dataset with anti-inflammatory potential activity.. In Proceedings of the MOL2NET, International Conference on Multidisciplinary Sciences, 25 December 2016–25 January 2017; Sciforum Electronic Conference Series, Vol. 2, 2016 ; doi:10.3390/mol2net-02-03859

Presentation

Author biographies

Luciana Scotti
 Graduate, master degree, PhD and post-doctor at University of São Paulo.  4 years post-doctor at Federal University of Paraíba. Experience in the field of Biochemistry and Molecular Pharmacology, working mainly in the following areas: natural products, Molecular Modeling, QSAR and Chemometrics.
Marcus Scotti
Prof. Marcus Tullius Scotti studied chemical engineering at Universidade de São Paulo (USP - São Paulo University) and finished his degree in 1999. After, he worked for four years in a brazilian electronics and telecommunications services company called Gradiente. At the same time he started to study specialization on Industrial Administration at University of São Paulo. After that, he started post-graduation in organic chemistry at the University of São Paulo in 2003, and finished his Master in 2005 and PhD in 2008. In January of 2009 he moved to João Pessoa and started to work as Professor of Organic Chemistry at Universidade Federal da Paraíba (Federal University of Paraíba), Brazil. At beginning of 2014 finished Pos-doc in cheminformatics at Universidade Nova de Lisboa, Portugal, Prof. Marcus research interests are in the area of chemistry of the natural products, acting on the following subjects: QSAR, Virtual Screening, molecular descriptors and chemotaxonomy using cheminformatics methods using several statistic tools and machine learning algorithms. Published 57 papers, 6 chapters book and 145 abstracts in conferences.

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