Journal
MOLECULAR PHARMACEUTICS
Volume 9, Issue 10, Pages 2912-2923Publisher
AMER CHEMICAL SOC
DOI: 10.1021/mp300237z
Keywords
QSAR; artificial neural networks; molecular fingerprints; bioactivity prediction; cannabinoid; CB2; virtual screening
Funding
- NIH [R01DA025612, R21HL109654, P50GM067082]
- NIH as part of the HHMI-NIBIB Interfaces Initiative under the Joint CMU-Pitt Computational Biology Ph.D. program at the Carnegie Mellon University [T32 EB009403]
- University of Pittsburgh
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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 CB2 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 CB2 binding affinities ranging from 6.70 nM to 3.75 mu M. 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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