Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions
Open accessAbstract
In this manuscript, we have reported a novel 2D fingerprint-based artificial neural network QSAR (FANN-QSAR) method in order to effectively predict biological activities of structurally diverse chemical ligands. Three different types of fingerprints, namely, ECFP6, FP2 and MACCS, were used in FANN-QSAR algorithm development, and FANN-QSAR models were compared to known 3D and 2D QSAR methods using five data sets previously reported. In addition, the derived models were used to predict GPCR cannabinoid ligand binding affinities using our manually curated cannabinoid ligand database containing 1699 structurally diverse compounds with reported cannabinoid receptor subtype CB(2) activities. To demonstrate its useful applications, the established FANN-QSAR algorithm was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds, and we have discovered several compounds with good CB(2) binding affinities ranging from 6.70 nM to 3.75 μM. To the best of our knowledge, this is the first report for a fingerprint-based neural network approach validated with a successful virtual screening application in identifying lead compounds. 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 [114]
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- Authors
- Myint, K.-Z., Wang, L., Tong, Q., & Xie, X.-Q.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Xie, Xiang-Qun
- Venue
- Molecular Pharmaceutics
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-08-31
- Volume
- 9
- Issue
- 10
- Pages
- 2912-2923
- ISSN
- 1543-8384 (ISSN-L)
- 1543-8392
- PMC
- PMC3462244
- OpenAlex
- W2081709350
- Cited by
- 139
- References
- 64
- Citations by year
- 2026: 5 · 2025: 17 · 2024: 8 · 2023: 16 · 2022: 12 · 2021: 9 · 2020: 19 · 2019: 11 · 2018: 14 · 2017: 4 · 2016: 13 · 2015: 1 · 2014: 6 · 2013: 2 · 2012: 2
- FWCI
- 4.12
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Molecular Pharmaceutics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Howard Hughes Medical Institute
- University of Pittsburgh
- Carnegie Mellon University
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R21HL109654)
- National Institute on Drug Abuse (R01DA025612)
- National Institute of General Medical Sciences (P50GM067082)
- National Institute of Biomedical Imaging and Bioengineering (T32EB009403)
- 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 › Cholinesterase and Neurodegenerative Diseases
- Keywords
- molecular fingerprints, artificial neural networks, CB2 receptor, cannabinoid ligands, virtual screening, MACCS, lead compound identification
- MeSH terms
- Dermatoglyphics, Ligands, Models, Chemical, Databases, Factual, Neural Networks, Computer, Quantitative Structure-Activity Relationship, Receptor, Cannabinoid, CB2, Drug Discovery
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