Cited by 24Computational Drug Discovery MethodsMachine Learning in Materials ScienceCannabis and Cannabinoid Research

Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR)

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Myint, Kyaw Z., Xie, Xiang-Qun·2015·Methods in molecular biology

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.

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Authors
Myint, K. Z., & Xie, X.-Q.
Affiliations
University of Pittsburgh (US)
Corresponding author
  • Myint, Kyaw Z.
  • Xie, Xiang-Qun
Venue
Methods in molecular biology
Published
2014-11-21
Volume
1260
Pages
149-164
ISSN
  • 1064-3745 (ISSN-L)
  • 1940-6029
OpenAlex
W2414116852
Cited by
21
References
60
Citations by year
2026: 3 · 2024: 1 · 2023: 2 · 2022: 4 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 4 · 2017: 1 · 2016: 1 · 2015: 1
FWCI
3.52
Type
book chapter
Language
en
Open access
Locations
  • Methods in molecular biology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Heart, Lung, and Blood Institute (R21HL109654)
  • National Institute on Drug Abuse (R01DA025612, P30DA035778)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Health Sciences › Medicine › Pharmacology › Cannabis and Cannabinoid Research
Keywords
molecular fingerprints, MACCS, bioactivity prediction, cannabinoid ligands, virtual screening, lead compound identification
MeSH terms
Algorithms, Cannabinoids, Ligands, Models, Molecular, Protein Binding, Neural Networks, Computer, Computational Biology, Quantitative Structure-Activity Relationship, Receptors, Cannabinoid, Databases, Chemical

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