ArticleJournal of chemical information and modeling2024
Structure-Based Drug Design with a Deep Hierarchical Generative Model.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Circumventing the Synthesizability Problem in Generative Molecular Design.Journal of chemical information and modeling · 2026Article
- Applying Deep-Learning-DrivenJournal of medicinal chemistry · 2026Article
- Chemical adaptation: bridging synthetic chemistry with drug development.Science China. Life sciences · 2026Review
- Recent Advances in Computer-Aided Drug Design and Drug Discovery.Molecules (Basel, Switzerland) · 2026Article
- From mechanistic modeling to AI-driven design: computational strategies for targeting the γ-secretase complex.Briefings in bioinformatics · 2026Review
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- Circumventing the synthesizability problem in generative molecular design.bioRxiv : the preprint server for biology · 2026Article
- Green Drug Discovery: Leveraging Biodiversity for Sustainable Pharmaceutical Solutions.Chemistry & biodiversity · 2026Review
- Synthetic pancreatic lipase inhibitors in obesity treatment: current updates onRSC medicinal chemistry · 2025Review
- Transcriptome-conditioned molecule generation via gene interaction-aware fragment modeling with a GPT-based architecture.Scientific reports · 2025Article
- Assay2Mol: Large Language Model-based Drug Design Using BioAssay Context.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- Article
- BInD: Bond and Interaction-Generating Diffusion Model for Multi-Objective Structure-Based Drug Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Identification of STAT3 phosphorylation inhibitors using generative deep learning, virtual screening, molecular dynamics simulations, and biological evaluation for non-small cell lung cancer therapy.Molecular diversity · 2025Article
- Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products.Natural products and bioprospecting · 2025Review
- MolSnapper: Conditioning Diffusion for Structure-Based Drug Design.Journal of chemical information and modeling · 2025Article
- Rag2Mol: structure-based drug design based on retrieval augmented generation.Briefings in bioinformatics · 2025Article
- Network pharmacology and AI in cancer research uncovering biomarkers and therapeutic targets for RALGDS mutations.Scientific reports · 2025Article
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2 authors.
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Abstract
Recently, the remarkable growth of available crystal structure data and libraries of commercially available or readily synthesizable molecules have unlocked previously inaccessible regions of chemical space for drug development. Paired with improvements in virtual ligand screening methods, these expanded libraries are having a notable impact on early drug design efforts. Yet screening-based methods still face scalability limits, due to computational constraints and the sheer scale of drug-like space. Machine learning approaches are overcoming these limitations by learning the fundamental intra- and intermolecular relationships in drug-target systems from existing data. Here, we introduce DrugHIVE, a deep hierarchical variational autoencoder that outperforms state-of-the-art autoregressive and diffusion-based methods in both speed and performance on common generative benchmarks. DrugHIVE's hierarchical design enables improved control over molecular generation. Its capabilities include dramatically increasing virtual screening efficiency and accelerating a wide range of common drug design tasks, including de novo generation, molecular optimization, scaffold hopping, linker design, and high-throughput pattern replacement. Our highly scalable method can even be applied to receptors with high-confidence AlphaFold-predicted structures, extending the ability to generate high-quality drug-like molecules to a majority of the unsolved human proteome.
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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.