ArticleJournal of chemical information and modeling2020
Deep Generative Models for 3D Linker Design.
Article in Journal of chemical information and modeling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.
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Who cites it
96 citing papers in PubMed.
- DesignMaster: a multi-conditional diffusion framework for rational PROTAC design.Bioinformatics (Oxford, England) · 2026Article
- Advancing PROTAC therapeutics through chemistry-guided design of smart delivery systems.Acta pharmacologica Sinica · 2026Review
- Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi-scenario applications, and translational prospects.Smart molecules : open access · 2026Review
- ConfRetro: a 3D-aware template-free method for enhancing retrosynthesis via molecular conformer information.Bioinformatics (Oxford, England) · 2026Article
- ActivityDiff: a diffusion model with positive and negative activity guidance for de novo drug design.Bioinformatics (Oxford, England) · 2026Article
- 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
- EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties.Journal of cheminformatics · 2026Article
- FLOWR.ROOT - A flow matching-based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction.Nature communications · 2026Article
- Deep learning for small-molecule drug discovery: From molecular design to clinical translation.Journal of pharmaceutical analysis · 2026Review
- Chemical neighborhood exploration for substrate discovery in biocatalysis.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- 3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table.Journal of chemical information and modeling · 2026Article
- Fragment-Based Design of Targeted Covalent Inhibitors: The Scope and Limitation of Linking Approaches.ChemMedChem · 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
- LinkLlama: Enabling Large Language Model for Chemically Reasonable Linker Design.bioRxiv : the preprint server for biology · 2026Article
- Folded-state compatibility and unfolded-state constraint govern staple-based stabilization: guidelines from a coiled-coil model.RSC chemical biology · 2026Article
- Generative AI in structure-based drug discovery.BMC biology · 2026Review
- DeepDegradome: A structure-aware deep learning framework for PROTAC and ligand generation against protein targets.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Frags2Drugs: A Novel In Silico Fragment-Based Approach to the Discovery of Kinase Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026Article
36 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of three-dimensional (3D) structural information. We have developed a novel graph-based deep generative model that combines state-of-the-art machine learning techniques with structural knowledge. Our method ("DeLinker") takes two fragments or partial structures and designs a molecule incorporating both. The generation process is protein-context-dependent, utilizing the relative distance and orientation between the partial structures. This 3D information is vital to successful compound design, and we demonstrate its impact on the generation process and the limitations of omitting such information. In a large-scale evaluation, DeLinker designed 60% more molecules with high 3D similarity to the original molecule than a database baseline. When considering the more relevant problem of longer linkers with at least five atoms, the outperformance increased to 200%. We demonstrate the effectiveness and applicability of this approach on a diverse range of design problems: fragment linking, scaffold hopping, and proteolysis targeting chimera (PROTAC) design. As far as we are aware, this is the first molecular generative model to incorporate 3D structural information directly in the design process. The code is available at https://github.com/oxpig/DeLinker.
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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.