ArticleInternational journal of molecular sciences2022
Machine Learning-Based Virtual Screening for the Identification of Cdk5 Inhibitors.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
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Who cites it
20 citing papers in PubMed, 31 citations in OpenAlex.
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- Machine learning-enhanced QSAR modeling for predicting drug efficacy against the RET V804M kinase domain mutation.Molecular diversity · 2026Article
- Artificial Intelligence in Selected Domains of Drug Discovery: A Critical Narrative Review.Drug design, development and therapy · 2026Review
- Computer-aided Drug Design for Alzheimer's Disease: Recent Advancements and Future Perspectives.Current topics in medicinal chemistry · 2026Review
- Machine Learning-Based Virtual Screening for the Identification of Novel CDK-9 Inhibitors.Biomolecules · 2025Article
- Identification of potent inhibitors of potential VEGFR2: a graph neural network-based virtual screening andJournal of enzyme inhibition and medicinal chemistry · 2025Article
- Staphylococcal nuclease and tudor domain-containing protein 1: An emerging therapeutic target in cancer (Review).Molecular and clinical oncology · 2025Review
- A Machine Learning Platform for Isoform-Specific Identification and Profiling of Human Carbonic Anhydrase Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2025Article
- KinasePred: A Computational Tool for Small-Molecule Kinase Target Prediction.International journal of molecular sciences · 2025Article
- A series of benzensulfonamide derivatives as new potent carbonic anhydrase IX and XII inhibitors.Future medicinal chemistry · 2025Article
- Prognostic Impact of Metabolic Syndrome and Steatotic Liver Disease in Hepatocellular Carcinoma Using Machine Learning Techniques.Metabolites · 2024Article
- In Silico Prediction of New Inhibitors for Kirsten Rat Sarcoma G12D Cancer Drug Target Using Machine Learning-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Approaches.Pharmaceuticals (Basel, Switzerland) · 2024Article
- VenomPred 2.0: A NovelJournal of chemical information and modeling · 2024Article
- Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the KRAS drug target.BMC chemistry · 2024Article
- Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment.Frontiers in chemistry · 2024Article
- Identification of New GSK3β Inhibitors through a Consensus Machine Learning-Based Virtual Screening.International journal of molecular sciences · 2023Article
- An in silico toolbox for the prediction of the potential pathogenic effects of missense mutations in the dimeric region ofJournal of enzyme inhibition and medicinal chemistry · 2023Article
- In Silico Characterization ofACS omega · 2023Article
- Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening.Molecules (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
Abstract
Cyclin-dependent kinase 5 (Cdk5) is an atypical proline-directed serine/threonine protein kinase well-characterized for its role in the central nervous system rather than in the cell cycle. Indeed, its dysregulation has been strongly implicated in the progression of synaptic dysfunction and neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), and also in the development and progression of a variety of cancers. For this reason, Cdk5 is considered as a promising target for drug design, and the discovery of novel small-molecule Cdk5 inhibitors is of great interest in the medicinal chemistry field. In this context, we employed a machine learning-based virtual screening protocol with subsequent molecular docking, molecular dynamics simulations and binding free energy evaluations. Our virtual screening studies resulted in the identification of two novel Cdk5 inhibitors, highlighting an experimental hit rate of 50% and thus validating the reliability of the in silico workflow. Both identified ligands, compounds
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What OpenQuestion holds
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.