ArticleNature computational science2024
Structure-based drug design with equivariant diffusion models.
Article in Nature computational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 155 papers.
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Who cites it
155 citing papers in PubMed.
- DesignMaster: a multi-conditional diffusion framework for rational PROTAC design.Bioinformatics (Oxford, England) · 2026Article
- A Multi-Scale Computational Framework for Anti-Persister Tuberculosis Drug Discovery.Chemical biology & drug design · 2026Review
- Enhanced Sampling of Protein Conformations in AlphaFold3 with Repulsive Bias in the Diffusion Generative Model.JACS Au · 2026Article
- Multiobjective Fluorescent Molecule Design with a Data-Physics Dual-Driven Generative Framework.JACS Au · 2026Article
- Reimagining computational macromolecular modeling: AI-driven approaches.Biophysical journal · 2026Review
- PockLigGPT: Pocket-Sequence-Conditioned Molecular Generation with GPTs and RL.Journal of chemical information and modeling · 2026Article
- GEM-GPT Enables Personalized Cell Type-Resolved Therapeutic Design for Systems Pharmacology.bioRxiv : the preprint server for biology · 2026Article
- Controllable molecular generation with fine-tuned flow-matching model.Communications chemistry · 2026Article
- GN-Net: A Geometric and Neighborhood-Aware Network for Predicting Intracranial Aneurysm Rupture Risk and Assisting Clinical Decision-Making.Interdisciplinary sciences, computational life sciences · 2026Article
- Beyond the flat screen: evaluating virtual reality as a complementary tool for molecular docking in drug discovery.Journal of computer-aided molecular design · 2026Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- Teaching diffusion models physics: reinforcement learning for physically valid diffusion-based docking.Chemical science · 2026Article
- Addressing antimicrobial resistance: Current challenges, emerging strategies, and an AI-powered, community-driven approach.PNAS nexus · 2026Review
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Mol-CADiff: text-conditional molecule generation via causality-aware autoregressive diffusion.Nature communications · 2026Article
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Circumventing the Synthesizability Problem in Generative Molecular Design.Journal of chemical information and modeling · 2026Article
- Fragment-based diffusion modeling and molecular dynamics simulation validation for the discovery of PD-L1 small-molecule inhibitors.Molecular diversity · 2026Article
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
95 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. Generative SBDD methods leverage structural data of drugs with their protein targets to propose new drug candidates. However, most existing methods focus exclusively on bottom-up de novo design of compounds or tackle other drug development challenges with task-specific models. The latter requires curation of suitable datasets, careful engineering of the models and retraining from scratch for each task. Here we show how a single pretrained diffusion model can be applied to a broader range of problems, such as off-the-shelf property optimization, explicit negative design and partial molecular design with inpainting. We formulate SBDD as a three-dimensional conditional generation problem and present DiffSBDD, an SE(3)-equivariant diffusion model that generates novel ligands conditioned on protein pockets. Furthermore, we show how additional constraints can be used to improve the generated drug candidates according to a variety of computational metrics.
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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.