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Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

  • Kyaw Z. MyintEmail author
  • Xiang-Qun XieEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1260)

Abstract

This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.

Key words

Quantitative structure-activity relationship (QSAR) Fingerprint Artificial neural networks (ANN) Biological activity Cannabinoid 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.NIDA Center of Excellence for Computational Chemogenomics Drug Abuse Research, Computational Chemical Genomics Screening CenterDepartment of Pharmaceutical Sciences, School of Pharmacy, University of PittsburghPittsburghUSA

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