ArticleInternational journal of molecular sciences2024
A Machine Learning Algorithm Suggests Repurposing Opportunities for Targeting Selected GPCRs.
Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Structural Basis for D3/D4-Selective Antagonism of Piperazinylalkyl Pyrazole/Isoxazole Analogs.Molecules (Basel, Switzerland) · 2025Article
- Advancing genome-based precision medicine: a review on machine learning applications for rare genetic disorders.Briefings in bioinformatics · 2025Review
- Recent trends in machine learning and deep learning-based prediction of G-protein coupled receptor-ligand binding affinities.Frontiers in bioinformatics · 2025Review
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Authors and funding
2 authors.
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Abstract
Repurposing utilizes existing drugs with known safety profiles and discovers new uses by combining experimental and computational approaches. The integration of computational methods has greatly advanced drug repurposing, offering a rational approach and reducing the risk of failure in these efforts. Recognizing the potential for drug repurposing, we employed our Iterative Stochastic Elimination (ISE) algorithm to screen known drugs from the DrugBank database. Repurposing in our hands is based on computer models of the actions of ligands: the ISE algorithm is a machine learning tool that creates ligand-based models by distinguishing between the physicochemical properties of known drugs and those of decoys. The models are large sets of "filters" made out, each, of molecular properties. We screen and score external sets of molecules (in our case- the DrugBank molecules) by our agonism and antagonism models based on published data (i.e., IC
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