ArticleMolecular pharmaceutics2019
Prediction of Orthosteric and Allosteric Regulations on Cannabinoid Receptors Using Supervised Machine Learning Classifiers.
Article in Molecular pharmaceutics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Target-driven machine learning-enabled virtual screening (TAME-VS) enables prioritization of AKR1C3 inhibitors.Communications chemistry · 2026Article
- 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
- DeepAllo: allosteric site prediction using protein language model (pLM) with multitask learning.Bioinformatics (Oxford, England) · 2025Article
- GCN-BBB: Deep Learning Blood-Brain Barrier (BBB) Permeability PharmacoAnalytics with Graph Convolutional Neural (GCN) Network.The AAPS journal · 2025Article
- A Structure-Based Allosteric Modulator Design Paradigm.Health data science · 2023Review
- Machine Learning and Computational Chemistry for the Endocannabinoid System.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Target-driven machine learning-enabled virtual screening (TAME-VS) platform for early-stage hit identification.Frontiers in molecular biosciences · 2023Article
- How Do Modulators Affect the Orthosteric and Allosteric Binding Pockets?ACS chemical neuroscience · 2022Article
- GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling.Journal of chemical theory and computation · 2022Article
- Article
- PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning.Frontiers in molecular biosciences · 2022Article
- PASSer: Prediction of Allosteric Sites Server.Machine learning: science and technology · 2021Article
- Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence.Biomolecules · 2021Article
- Generative chemistry: drug discovery with deep learning generative models.Journal of molecular modeling · 2021Review
- Covalent allosteric modulation: An emerging strategy for GPCRs drug discovery.European journal of medicinal chemistry · 2020Review
- Structural Insights into β-arrestin/CB1 Receptor Interaction: NMR and CD Studies on Model Peptides.International journal of molecular sciences · 2020Article
- Annotation of Allosteric Compounds to Enhance Bioactivity Modeling for Class A GPCRs.Journal of chemical information and modeling · 2020Article
- Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.Methods (San Diego, Calif.) · 2020Review
- Novel approaches and current challenges with targeting the endocannabinoid system.Expert opinion on drug discovery · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Designing highly selective compounds to protein subtypes and developing allosteric modulators targeting them are critical considerations to both drug discovery and mechanism studies for cannabinoid receptors. It is challenging but in demand to have classifiers to identify active ligands from inactive or random compounds and distinguish allosteric modulators from orthosteric ligands. In this study, supervised machine learning classifiers were built for two subtypes of cannabinoid receptors, CB1 and CB2. Three types of features, including molecular descriptors, MACCS fingerprints, and ECFP6 fingerprints, were calculated to evaluate the compound sets from diverse aspects. Deep neural networks, as well as conventional machine learning algorithms including support vector machine, naïve Bayes, logistic regression, and ensemble learning, were applied. Their performances on the classification with different types of features were compared and discussed. According to the receiver operating characteristic curves and the calculated metrics, the advantages and drawbacks of each algorithm were investigated. The feature ranking was followed to help extract useful knowledge about critical molecular properties, substructural keys, and circular fingerprints. The extracted features will then facilitate the research on cannabinoid receptors by providing guidance on preferred properties for compound modification and novel scaffold design. Besides using conventional molecular docking studies for compound virtual screening, machine-learning-based decision-making models provide alternative options. This study can be of value to the application of machine learning in the area of drug discovery and compound development.
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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.