ArticleBriefings in bioinformatics2024
DiffPROTACs is a deep learning-based generator for proteolysis targeting chimeras.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- DesignMaster: a multi-conditional diffusion framework for rational PROTAC design.Bioinformatics (Oxford, England) · 2026Article
- From Ternary Modeling to Predictive PROTAC Design: A Computational Perspective.Journal of chemical information and modeling · 2026Article
- AI-Driven Multi-Omics Integration of Synthetic Colon Adenocarcinoma for Cluster-Guided PROTAC Candidate Design Targeting KRASG12D.International journal of molecular sciences · 2026Article
- Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi-scenario applications, and translational prospects.Smart molecules : open access · 2026Review
- Design of Synthesizable PROTACs through Synthesis Constrained Generative Model and Reinforcement Learning.JACS Au · 2026Article
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- From undruggable to degradable: A deep learning-enabled framework for precision orthopaedic protein degradation.Journal of orthopaedic translation · 2026Review
- PROTAC-mediated multi-target protein degradation in Alzheimer's disease: mechanistic insights, therapeutic applications, and translational challenges.RSC medicinal chemistry · 2026Review
- Article
- A machine learning-based pharmacokinetics predictor (EGFR-PROPK) for EGFR-targeting PROTACs.Communications chemistry · 2026Article
- From concept to application: Exploring the evolution and potential of DUBTAC technology.Acta pharmaceutica Sinica. B · 2026Review
- 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
- PROTAC-Based Antivirals for Respiratory Viruses: A Novel Approach for Targeted Therapy and Vaccine Development.Microorganisms · 2025Review
- Recent advances in molecular representation methods and their applications in scaffold hopping.npj drug discovery · 2025Review
- Predicting PROTAC off-target effects via warhead involvement levels in drug-target interactions using graph attention neural networks.Computational and structural biotechnology journal · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
PROteolysis TArgeting Chimeras (PROTACs) has recently emerged as a promising technology. However, the design of rational PROTACs, especially the linker component, remains challenging due to the absence of structure-activity relationships and experimental data. Leveraging the structural characteristics of PROTACs, fragment-based drug design (FBDD) provides a feasible approach for PROTAC research. Concurrently, artificial intelligence-generated content has attracted considerable attention, with diffusion models and Transformers emerging as indispensable tools in this field. In response, we present a new diffusion model, DiffPROTACs, harnessing the power of Transformers to learn and generate new PROTAC linkers based on given ligands. To introduce the essential inductive biases required for molecular generation, we propose the O(3) equivariant graph Transformer module, which augments Transformers with graph neural networks (GNNs), using Transformers to update nodes and GNNs to update the coordinates of PROTAC atoms. DiffPROTACs effectively competes with existing models and achieves comparable performance on two traditional FBDD datasets, ZINC and GEOM. To differentiate the molecular characteristics between PROTACs and traditional small molecules, we fine-tuned the model on our self-built PROTACs dataset, achieving a 93.86% validity rate for generated PROTACs. Additionally, we provide a generated PROTAC database for further research, which can be accessed at https://bailab.siais.shanghaitech.edu.cn/service/DiffPROTACs-generated.tgz. The corresponding code is available at https://github.com/Fenglei104/DiffPROTACs and the server is at https://bailab.siais.shanghaitech.edu.cn/services/diffprotacs.
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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.