ArticleACS omega2023
Predictive Modeling of PROTAC Cell Permeability with Machine Learning.
Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- From prediction to performance:International journal of pharmaceutics: X · 2026Article
- Advancing PROTAC therapeutics through chemistry-guided design of smart delivery systems.Acta pharmacologica Sinica · 2026Review
- Induced proximity-based therapeutics for advanced prostate cancer.npj drug discovery · 2026Review
- Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi-scenario applications, and translational prospects.Smart molecules : open access · 2026Review
- Methods to Study the Molecular Mechanism and Drive the Design of Protein Degraders.Chemical reviews · 2026Review
- Integrating AI into next-generation PROTAC Engineering: a comprehensive toolkit for rational PROTAC design.Molecular diversity · 2026Review
- From undruggable to degradable: A deep learning-enabled framework for precision orthopaedic protein degradation.Journal of orthopaedic translation · 2026Review
- Article
- Modulation of the tumor microenvironment by the ubiquitin-proteasome system in colorectal cancer.Journal of translational medicine · 2025Review
- Advancing PROTAC Discovery Through Artificial Intelligence: Opportunities, Challenges, and Future Directions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- AI-Integrated QSAR Modeling for Enhanced Drug Discovery: From Classical Approaches to Deep Learning and Structural Insight.International journal of molecular sciences · 2025Review
- AI-DPAPT: a machine learning framework for predicting PROTAC activity.Molecular diversity · 2025Article
- New Multiparameter Index Is a Strong Predictor of Oral Bioavailability for Heterobifunctional Degraders.ACS medicinal chemistry letters · 2025Article
- Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models.Pain and therapy · 2025Article
- Impact of Linker Composition on VHL PROTAC Cell Permeability.Journal of medicinal chemistry · 2025Article
- A membrane permeability database for nonpeptidic macrocycles.Scientific data · 2025Article
- Rationalize the Functional Roles of Protein-Protein Interactions in Targeted Protein Degradation by Kinetic Monte-Carlo Simulations.bioRxiv : the preprint server for biology · 2024Article
- Review
- Precision oncology revolution: CRISPR-Cas9 and PROTAC technologies unleashed.Frontiers in genetics · 2024Review
- Revolutionizing Drug Targeting Strategies: Integrating Artificial Intelligence and Structure-Based Methods in PROTAC Development.Pharmaceuticals (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Approaches for predicting proteolysis targeting chimera (PROTAC) cell permeability are of major interest to reduce resource-demanding synthesis and testing of low-permeable PROTACs. We report a comprehensive investigation of the scope and limitations of machine learning-based binary classification models developed using 17 simple descriptors for large and structurally diverse sets of cereblon (CRBN) and von Hippel-Lindau (VHL) PROTACs. For the VHL PROTAC set, kappa nearest neighbor and random forest models performed best and predicted the permeability of a blinded test set with >80% accuracy (
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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.