ArticleScientific reports2019
Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 34 citations in OpenAlex.
- Pharmacological Targeting of Peroxisome Proliferator-activated Receptors for Prevention of Radiation-induced Cognitive Decline.Annals of neurosciences · 2026Review
- AHR/NRF2 Dual Agonist Prediction and Natural Compound Screening Based on Machine Learning: A New Strategy for the Treatment of Atopic Dermatitis.International journal of molecular sciences · 2026Article
- Promoting collagen synthesis: a viable strategy to combat skin ageing.Journal of enzyme inhibition and medicinal chemistry · 2025Review
- Unraveling PPARβ/δ nuclear receptor agonistsRSC advances · 2025Article
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- Synergizing Experimentation and Computation: Predicting Energetic Potential in New Cyclo-Peroxide Compounds.ACS omega · 2024Article
- From Sequence to Solution: Intelligent Learning Engine Optimization in Drug Discovery and Protein Analysis.Biotech (Basel (Switzerland)) · 2024Article
- Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists.International journal of molecular sciences · 2024Article
- Lipodystrophy in methylmalonic acidemia associated with elevated FGF21 and abnormal methylmalonylation.JCI insight · 2024Article
- Highly potent and selective PPARδ agonist reverses memory deficits in mouse models of Alzheimer's disease.Theranostics · 2024Article
- Addressing chemically-induced obesogenic metabolic disruption: selection of chemicals forFrontiers in endocrinology · 2024Review
- Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes.Journal of chemical information and modeling · 2023Article
- Nuclear Receptors in Energy Metabolism.Advances in experimental medicine and biology · 2022Article
- Article
- Candidate Therapeutics by Screening for Multitargeting Ligands: Combining the CB2 Receptor With CB1, PPARγ and 5-HT4 Receptors.Frontiers in pharmacology · 2022Article
- PPARdelta in Affected Atopic Dermatitis and Psoriasis: A Possible Role in Metabolic Reprograming.International journal of molecular sciences · 2021Review
- A review on machine learning approaches and trends in drug discovery.Computational and structural biotechnology journal · 2021Review
- Article
- Review
- Computational design of substrate selective inhibition.PLoS computational biology · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called "Iterative Stochastic Elimination" (ISE) was applied to construct a ligand-based multi-filter ranking model to distinguish between confirmed PPAR-δ agonists and random molecules. Virtual screening of 1.56 million molecules by this model picked ~2500 top ranking molecules. Subsequent docking to PPAR-δ structures was mainly evaluated by geometric analysis of the docking poses rather than by energy criteria, leading to a set of 306 molecules that were sent for testing in vitro. Out of those, 13 molecules were found as potential PPAR-δ agonist leads with EC
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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.