ArticleNature communications2025
Target-aware 3D molecular generation based on guided equivariant diffusion.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A Site-Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Controllable molecular generation with fine-tuned flow-matching model.Communications chemistry · 2026Article
- A dynamic fusion diffusion model for molecular generation.Molecular diversity · 2026Article
- Strategy and efficiency-redefined discovery of novel nanomolar tyrosinase inhibitors: AI de novo molecular generation + expert-guided structural optimization.Journal of advanced research · 2026Article
- Generation of antifungals to combat drug resistance using language models and diffusion models.Journal of advanced research · 2026Article
- ScrambleBench: a workflow for comparative assessment of structure-based de novo generative models.Journal of cheminformatics · 2026Article
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- Machine-Learning-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry.Molecules (Basel, Switzerland) · 2026Review
- Assessing the factors influencing the quality of pocket-conditioned 3D generative models.Journal of cheminformatics · 2026Article
- Review
- DDA: a traceable multi-agent framework for automated structure-based drug design.Frontiers in chemistry · 2026Article
- Artificial Intelligence as a Disruptive Force in Pharmaceutical Innovation: Transforming Discovery, Development, and Manufacturing.Drug design, development and therapy · 2026Review
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Recent molecular generation models for structure-based drug design (SBDD) often produce unrealistic 3D molecules due to the neglect of structural feasibility and drug-like properties. In this paper, we introduce DiffGui, a target-conditioned E(3)-equivariant diffusion model that integrates bond diffusion and property guidance, to address the above challenges. The combination of atom diffusion and bond diffusion guarantees the concurrent generation of both atoms and bonds by explicitly modeling their interdependencies. Property guidance incorporates the binding affinity and drug-like properties of molecules into the training and sampling processes. Extensive experiments prove that DiffGui outperforms existing methods in generating molecules with high binding affinity, rational chemical structure, and desirable properties. Ablation studies confirm the importance of bond diffusion and property guidance modules. DiffGui demonstrates effectiveness in both de novo drug design and lead optimization, with validation through wet-lab experiments.
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Registered trials
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