ArticleJournal of chemical information and modeling2023
Fragment Linker Prediction Using the Deep Encoder-Decoder Network for PROTACs Drug Design.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Advancing PROTAC therapeutics through chemistry-guided design of smart delivery systems.Acta pharmacologica Sinica · 2026Review
- Aptamer-Based Heterobifunctional Targeted Degraders in Disease Treatment.Molecules (Basel, Switzerland) · 2026Review
- Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi-scenario applications, and translational prospects.Smart molecules : open access · 2026Review
- RelAgent: a multi-agent solution for molecular relationship grounding.Bioinformatics (Oxford, England) · 2026Article
- Design of Synthesizable PROTACs through Synthesis Constrained Generative Model and Reinforcement Learning.JACS Au · 2026Article
- Proteolysis-targeting chimera (PROTAC) in cancer: design principles and applications on "undruggable" targets.Biomarker research · 2026Review
- Integrating AI into next-generation PROTAC Engineering: a comprehensive toolkit for rational PROTAC design.Molecular diversity · 2026Review
- Article
- Interpretable PROTAC Degradation Prediction With Structure-Informed Deep Ternary Attention Framework.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Recent Advances in the Development of Pro-PROTAC for Selective Protein Degradation.Pharmaceutics · 2025Review
- Advancing Design Strategy of PROTACs for Cancer Therapy.MedComm · 2025Review
- Accurate PROTAC-targeted degradation prediction with DegradeMaster.Bioinformatics (Oxford, England) · 2025Article
- The Applications of Computational Tools in PROTAC Development.Current topics in medicinal chemistry · 2025Review
- Characteristic roadmap of linker governs the rational design of PROTACs.Acta pharmaceutica Sinica. B · 2024Review
- Article
- Revolutionizing Drug Targeting Strategies: Integrating Artificial Intelligence and Structure-Based Methods in PROTAC Development.Pharmaceuticals (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A drug discovery and development pipeline is a prolonged and complex process that remains challenging for both computational methods and medicinal chemists and has not been able to be resolved using computational methods. Deep learning has been utilized in various fields and achieved tremendous success in designing novel molecules in the pharmaceutical industry. Herein, we use state-of-the-art techniques to propose a deep neural network, AIMLinker, to rapidly design and generate meaningful drug-like proteolysis targeting chimeras (PROTACs) analogs. The model extracts the structural information from the input fragments and generates linkers to incorporate them. We integrate filters in the model to exclude nondruggable structures guided via protein-protein complexes while retaining molecules with potent chemical properties. The novel PROTACs subsequently pass through molecular docking, taking root-mean-square deviation (RMSD), relative Gibbs free energy (
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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.