ArticleCommunications chemistry2024
Geometry-complete diffusion for 3D molecule generation and optimization.
Article in Communications chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Ligand Design Using Unique Conformations to Preferentially Dock a Specific Site on Collagen-Bound MMP1.Biology · 2026Article
- Drug design using unique conformations to preferentially target a specific site on collagen-bound MMP1.bioRxiv : the preprint server for biology · 2026Article
- AI-driven fungicide design: From target identification to field application.Plant communications · 2026Review
- HybridMolGen: a unified framework for goal-directed molecular generation via multi-objective reinforcement learning.Bioinformatics (Oxford, England) · 2026Article
- Disentangled autoencoding equivariant diffusion model for controlled generation of 3D molecules.Nature communications · 2026Article
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional Materials.Journal of chemical theory and computation · 2026Article
- Unavailability of experimental 3D structural data on protein folding dynamics and necessity for a new generation of structure prediction methods in this context.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- Generative Chemistry Platform for Small Molecules Targeting RNA: A Case Study for Chemical Optimization.Computational and structural biotechnology journal · 2026Article
- Deep Generative AI for Multi-Target Therapeutic Design: Toward Self-Improving Drug Discovery Framework.International journal of molecular sciences · 2025Review
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- Stereochemistry-aware string-based molecular generation.PNAS nexus · 2025Article
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- Target-aware 3D molecular generation based on guided equivariant diffusion.Nature communications · 2025Article
- Generative artificial intelligence based models optimization towards molecule design enhancement.Journal of cheminformatics · 2025Review
- Geometry-complete latent diffusion model for 3D molecule generation.Bioinformatics (Oxford, England) · 2025Article
- Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach.Journal of pharmaceutical analysis · 2025Article
- DiffMC-Gen: A Dual Denoising Diffusion Model for Multi-Conditional Molecular Generation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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2 authors.
Funding
Abstract
Generative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively. Importantly, we demonstrate that GCDM's generative denoising process enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Code and data are freely available on GitHub .
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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.