Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport

• URL: http://arxiv.org/abs/2606.07239v1
• Date: Fri, 05 Jun 2026 13:07:56 GMT
• Title: Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport
• Abstract summary: We introduce Morph, a flexible-size generative model for conditional and unconditional 3D molecular design based on geometric graphs.<n>By dynamically adapting size, Morph can seamlessly integrate existing structural priors, like scaffolds, and significantly enhances property steering.<n>We show that Morph matches current fixed-size state-of-the-art models while offering the benefit of unparalleled sampling flexibility.
• Score: 27.245262040077716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The success of generative molecular design hinges on a model's steerability toward high-reward samples. Because many molecular properties are intrinsically linked to molecular size, accurately capturing the joint distribution of properties and the number of atoms is essential. However, current diffusion and flow-based models fix the number of atoms, which ultimately limits their ability to navigate this complex relationship. To address this, we introduce Morph, a flexible-size generative model for conditional and unconditional 3D molecular design based on geometric graphs. By dynamically adapting size, Morph can seamlessly integrate existing structural priors, like scaffolds, and significantly enhances property steering. We show that Morph matches current fixed-size state-of-the-art models while offering the benefit of unparalleled sampling flexibility. We demonstrate out-of-distribution generation in regimes where previous models fail, paving the way for enhanced generative modeling for molecular design.

Related papers

• FlexiFlow: decomposable flow matching for generation of flexible molecular ensemble [43.16267651837197]
  FlexiFlow is a novel architecture that extends flow-matching models.<n>We demonstrate the effectiveness of our approach on the QM9 and GEOM Drugs datasets.
  arXiv  Detail & Related papers  (2025-11-21T13:50:54Z)
• FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation [0.0]
  FlowMol3 is an open-source, multi-modal flow matching model that advances the state of the art for all-atom, small-molecule generation.<n>Our results highlight simple, transferable strategies for improving the stability and quality of diffusion- and flow-based molecular generative models.
  arXiv  Detail & Related papers  (2025-08-18T05:13:27Z)
• LMDM:Latent Molecular Diffusion Model For 3D Molecule Generation [6.720821935934759]
  We propose a latent molecular diffusion model that can make generated 3D molecules rich in diversity and maintain rich geometric features.<n>The model captures the information of the forces and local constraints between atoms so that the generated molecules can maintain Euclidean transformation.<n>In the experiment, the quality of the samples we generated and the convergence speed of the model have been significantly improved.
  arXiv  Detail & Related papers  (2024-12-05T15:25:18Z)
• DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization [49.85944390503957]
  DecompOpt is a structure-based molecular optimization method based on a controllable and diffusion model. We show that DecompOpt can efficiently generate molecules with improved properties than strong de novo baselines.
  arXiv  Detail & Related papers  (2024-03-07T02:53:40Z)
• Molecule Design by Latent Prompt Transformer [76.2112075557233]
  This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task. We propose a novel generative model comprising three components: (1) a latent vector with a learnable prior distribution; (2) a molecule generation model based on a causal Transformer, which uses the latent vector as a prompt; and (3) a property prediction model that predicts a molecule's target properties and/or constraint values using the latent prompt.
  arXiv  Detail & Related papers  (2024-02-27T03:33:23Z)
• Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting [53.44684898432997]
  Generation of molecules with desired chemical and biological properties is critical for drug discovery. We propose a probabilistic generative model to capture the joint distribution of molecules and their properties. Our method achieves very strong performances on various molecule design tasks.
  arXiv  Detail & Related papers  (2023-06-09T03:04:21Z)
• Geometric Latent Diffusion Models for 3D Molecule Generation [172.15028281732737]
  Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries. We propose a novel and principled method for 3D molecule generation named Geometric Latent Diffusion Models (GeoLDM)
  arXiv  Detail & Related papers  (2023-05-02T01:07:22Z)
• MUDiff: Unified Diffusion for Complete Molecule Generation [104.7021929437504]
  We present a new model for generating a comprehensive representation of molecules, including atom features, 2D discrete molecule structures, and 3D continuous molecule coordinates. We propose a novel graph transformer architecture to denoise the diffusion process. Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.
  arXiv  Detail & Related papers  (2023-04-28T04:25:57Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.