Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR)
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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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With molecular fingerprints ECFP6, FP2, MACCS combined with ANN models, two-dimensional QSAR virtual screening can achieve an average r test value of 0.75.
“With molecular fingerprints ECFP6, FP2, MACCS combined with ANN models, the two-dimensional QSAR virtual screening can achieve an average r test value (which measures regression fitness) of 0.75 [114,115].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [115]
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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
- Publisher
- Springer New York
- Published
- 2014-11-21
- Volume
- 1260
- Pages
- 149-164
- ISSN
- 1064-3745 (ISSN-L)
- 1940-6029
- PMC
- PMC4510302
- 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
- green
- www.ncbi.nlm.nih.gov
- Licence
- 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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