ArticleJournal of cheminformatics2023
PINNED: identifying characteristics of druggable human proteins using an interpretable neural network.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- The role of AI in oncology: present applications and future horizons.NPJ precision oncology · 2026Review
- Genomics of drug target prioritization for complex diseases.Nature reviews. Genetics · 2026Review
- Mendelian Randomization-Based Discovery of Novel Protein Biomarkers and Drug Targets in Colorectal Cancer: Validation Through Prognostic Modeling, Single-Cell Analysis, and In Vitro Cell Experiments.Applied biochemistry and biotechnology · 2025Article
- DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.Nucleic acids research · 2025Article
- Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review.International journal of molecular sciences · 2024Review
- Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses.Scientific reports · 2024Article
- Comprehensive Research on Druggable Proteins: From PSSM to Pre-Trained Language Models.International journal of molecular sciences · 2024Article
- BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins.Nucleic acids research · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of human proteins that are amenable to pharmacologic modulation without significant off-target effects remains an important unsolved challenge. Computational methods have been devised to identify features which distinguish between "druggable" and "undruggable" proteins, finding that protein sequence, tissue and cellular localization, biological role, and position in the protein-protein interaction network are all important discriminant factors. However, many prior efforts to automate the assessment of protein druggability suffer from low performance or poor interpretability. We developed a neural network-based machine learning model capable of generating druggability sub-scores based on each of four distinct categories, combining them to form an overall druggability score. The model achieves an excellent performance in separating drugged and undrugged proteins in the human proteome, with an area under the receiver operating characteristic (AUC) of 0.95. Our use of multiple sub-scores allows the assessment of potential protein targets of interest based on distinct contributors to druggability, leading to a more interpretable and holistic model to identify novel targets.
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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.