ArticleJournal of chemical information and modeling2013
LiCABEDS II. Modeling of ligand selectivity for G-protein-coupled cannabinoid receptors.
Article in Journal of chemical information and modeling, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 31 citations in OpenAlex.
- Leveraging machine learning for selective cannabinoid ligand discovery: methods, challenges, and opportunities.Expert opinion on drug discovery · 2026Review
- Predicting affinity and potency of new psychoactive substances at cannabinoid 1 receptor with explainable artificial intelligence.Frontiers in pharmacology · 2026Article
- Distinct activation mechanisms regulate subtype selectivity of Cannabinoid receptors.Communications biology · 2023Article
- Machine Learning and Computational Chemistry for the Endocannabinoid System.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence.Biomolecules · 2021Article
- Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research.Briefings in bioinformatics · 2021Article
- Neuroprotective derivatives of tacrine that target NMDA receptor and acetyl cholinesterase - Design, synthesis and biological evaluation.Computational and structural biotechnology journal · 2021Article
- Structural insight into the serotonin (5-HT) receptor family by molecular docking, molecular dynamics simulation and systems pharmacology analysis.Acta pharmacologica Sinica · 2019Article
- Prediction of Orthosteric and Allosteric Regulations on Cannabinoid Receptors Using Supervised Machine Learning Classifiers.Molecular pharmaceutics · 2019Article
- DAKB-GPCRs: An Integrated Computational Platform for Drug Abuse Related GPCRs.Journal of chemical information and modeling · 2019Article
- Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.The AAPS journal · 2018Review
- StemCellCKB: An Integrated Stem Cell-Specific Chemogenomics KnowledgeBase for Target Identification and Systems-Pharmacology Research.Journal of chemical information and modeling · 2016Article
- An Algorithm to Identify Target-Selective Ligands - A Case Study of 5-HT7/5-HT1A Receptor Selectivity.PloS one · 2016Article
- Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR).Methods in molecular biology (Clifton, N.J.) · 2015Article
- Chemogenomics knowledgebased polypharmacology analyses of drug abuse related G-protein coupled receptors and their ligands.Frontiers in pharmacology · 2014Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
The cannabinoid receptor subtype 2 (CB2) is a promising therapeutic target for blood cancer, pain relief, osteoporosis, and immune system disease. The recent withdrawal of Rimonabant, which targets another closely related cannabinoid receptor (CB1), accentuates the importance of selectivity for the development of CB2 ligands in order to minimize their effects on the CB1 receptor. In our previous study, LiCABEDS (Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps) was reported as a generic ligand classification algorithm for the prediction of categorical molecular properties. Here, we report extension of the application of LiCABEDS to the modeling of cannabinoid ligand selectivity with molecular fingerprints as descriptors. The performance of LiCABEDS was systematically compared with another popular classification algorithm, support vector machine (SVM), according to prediction precision and recall rate. In addition, the examination of LiCABEDS models revealed the difference in structure diversity of CB1 and CB2 selective ligands. The structure determination from data mining could be useful for the design of novel cannabinoid lead compounds. More importantly, the potential of LiCABEDS was demonstrated through successful identification of newly synthesized CB2 selective compounds.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.