ArticleiScience2022
Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The trial behind it
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Who cites it
24 citing papers in PubMed.
- A peripheral blood-based approach involving vimentin along with AURKA enabled efficient tracking of elusive Oct4/Sox2-expressing disseminated breast cancer stem cells.Bioscience reports · 2026Article
- Integrating evolutionary and compositional features with ML and DL for robust and interpretable druggable protein prediction.Journal of computer-aided molecular design · 2026Article
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- The role of AI in oncology: present applications and future horizons.NPJ precision oncology · 2026Review
- Molecular Mimicry Inference, Therapeutic Target Identification, and Natural Product Inhibition Study inACS omega · 2026Article
- Drug target mining and in silico screening of Tibetan plant metabolites for potential alleviation of Oroya fever, a neglected tropical disease.Scientific reports · 2026Article
- pCPPs-sADNN: predicting cell-penetrating peptides using self-attention based deep neural network.Scientific reports · 2025Article
- Aurora Kinase A inhibitor alisertib failed to exert its efficacy on TNBC cells due to consequential enrichment of polyploid giant cancer cells (PGCCs).Discover oncology · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Proteome mining of Yersinia Enterocolitica for drug targets and computational inhibitor identification with ADMET, anti-inflammation potential and formulation characteristics.BioData mining · 2025Article
- DrugProtAI: A machine learning-driven approach for predicting protein druggability through feature engineering and robust partition-based ensemble methods.Briefings in bioinformatics · 2025Article
- DrugTar improves druggability prediction by integrating large language models and gene ontologies.Bioinformatics (Oxford, England) · 2025Article
- GRU4ACE: Enhancing ACE inhibitory peptide prediction by integrating gated recurrent unit with multi-source feature embeddings.Protein science : a publication of the Protein Society · 2025Article
- M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy.BMC bioinformatics · 2025Article
- PMPred-AE: a computational model for the detection and interpretation of pathological myopia based on artificial intelligence.Frontiers in medicine · 2025Article
- Repurposing FDA-Approved Drugs Against Potential Drug Targets Involved in Brain Inflammation Contributing to Alzheimer's Disease.Targets (Basel) · 2024Article
- DPI_CDF: druggable protein identifier using cascade deep forest.BMC bioinformatics · 2024Article
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
- StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens.BMC bioinformatics · 2023Article
- PINNED: identifying characteristics of druggable human proteins using an interpretable neural network.Journal of cheminformatics · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Discovery of potential drugs requires rapid and precise identification of drug targets. Although traditional experimental methodologies can accurately identify drug targets, they are time-consuming and inappropriate for high-throughput screening. Computational approaches based on machine learning (ML) algorithms can expedite the prediction of druggable proteins; however, the performance of the existing computational methods remains unsatisfactory. This study proposes a computational tool, SPIDER, to enhance the accurate prediction of druggable proteins. SPIDER employs various feature descriptors pertaining to several aspects, including physicochemical properties, compositional information, and composition-transition-distribution information, coupled with well-known ML algorithms to facilitate the construction of the final meta-predictor. The experimental results showed that SPIDER enabled more precise and robust prediction of druggable proteins than the baseline models and current existing methods in terms of the independent test dataset. An online web server was established and made freely available online.
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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 Socratic graph.