Property-Guided Molecular Generation and Optimization via Latent Flows

• URL: http://arxiv.org/abs/2603.26889v1
• Date: Fri, 27 Mar 2026 18:09:52 GMT
• Title: Property-Guided Molecular Generation and Optimization via Latent Flows
• Abstract summary: MoltenFlow is a modular framework that combines property-organized latent representations with flow-matching priors and gradient-based guidance.<n>We show that guided latent flows enable efficient multi-objective molecular optimization under fixed oracle budgets with controllable trade-offs.
• Score: 44.50339042016925
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Molecular discovery is increasingly framed as an inverse design problem: identifying molecular structures that satisfy desired property profiles under feasibility constraints. While recent generative models provide continuous latent representations of chemical space, targeted optimization within these representations often leads to degraded validity, loss of structural fidelity, or unstable behavior. We introduce MoltenFlow, a modular framework that combines property-organized latent representations with flow-matching generative priors and gradient-based guidance. This formulation supports both conditioned generation and local optimization within a single latent-space framework. We show that guided latent flows enable efficient multi-objective molecular optimization under fixed oracle budgets with controllable trade-offs, while a learned flow prior improves unconditional generation quality.

Related papers

• Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization [85.4863852505905]
  We introduce unsupervised reward optimization of protein language models (PLMs)<n>Our key insight is that task-agnostic rewards, which combine intrinsic model uncertainty with semantic consistency informed by protein representation models, exhibit strong correlation with controllability measures across base models and temperature regimes.<n>Our framework provides a scalable pathway toward controllable biomolecular design in settings where labeled preferences or experimental feedback are scarce or unavailable.
  arXiv  Detail & Related papers  (2026-06-17T11:42:01Z)
• Topological Flow Matching [56.16372813551555]
  Flow matching is a powerful generative modeling framework, valued for its simplicity and strong empirical performance.<n>We introduce topological flow matching, a topology-aware generalization of flow matching.<n>We demonstrate its effectiveness on diverse structured datasets, including brain fMRIs, ocean currents, seismic events, and traffic flows.
  arXiv  Detail & Related papers  (2026-06-14T16:29:33Z)
• Constrained Flow Optimization via Sequential Fine Tuning for Molecular Design [31.59334876687123]
  We present a framework for Constrained Generative Optimization (CFO)<n>CFO balances reward and constraint satisfaction by reducing the original problem to sequential fine-tuning.<n>We provide convergence guarantees for constrained generative optimization and constrained generation via CFO.
  arXiv  Detail & Related papers  (2026-05-28T22:02:57Z)
• LEGO-MOF: Equivariant Latent Manipulation for Editable, Generative, and Optimizable MOF Design [11.084397378671682]
  Metal-organic frameworks (MOFs) are highly promising for carbon capture, yet navigating their vast design space remains challenging.<n>Recent deep generative models enable de novo MOF design but primarily act as feed-forward structure generators.<n>Here, we propose a target-driven generative framework focused on continuous structural manipulation.
  arXiv  Detail & Related papers  (2026-04-15T06:06:11Z)
• MolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow Matching [6.649784863468093]
  Key objectives in conditional molecular generation include ensuring chemical validity, aligning generated molecules with target properties, and enabling efficient sampling for discovery.<n>Recent advances in computer vision introduced a range of new guidance strategies for generative models.<n>We integrate state-of-the-art guidance methods in a leading molecule generation framework built on an SE(3)-equivariant flow matching process.
  arXiv  Detail & Related papers  (2025-12-13T06:05:09Z)
• TempFlow-GRPO: When Timing Matters for GRPO in Flow Models [22.023027865557637]
  We introduce a principled GRPO framework that captures and exploits the temporal structure inherent in flow-based generation.<n>New innovations endow the model with temporally-aware optimization that respects the underlying generative dynamics.
  arXiv  Detail & Related papers  (2025-08-06T11:10:39Z)
• Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models [93.76814568163353]
  We propose a novel bilevel optimization framework for pruned diffusion models.<n>This framework consolidates the fine-tuning and unlearning processes into a unified phase.<n>It is compatible with various pruning and concept unlearning methods.
  arXiv  Detail & Related papers  (2024-12-19T19:13:18Z)
• Improving Molecular Graph Generation with Flow Matching and Optimal Transport [8.2504828891983]
  GGFlow is a discrete flow matching generative model incorporating optimal transport for molecular graphs. It incorporates an edge-augmented graph transformer to enable the direct communications among chemical bounds. GGFlow demonstrates superior performance on both unconditional and conditional molecule generation tasks.
  arXiv  Detail & Related papers  (2024-11-08T16:27:27Z)
• Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization [147.7899503829411]
  AliDiff is a novel framework to align pretrained target diffusion models with preferred functional properties. It can generate molecules with state-of-the-art binding energies with up to -7.07 Avg. Vina Score.
  arXiv  Detail & Related papers  (2024-07-01T06:10:29Z)
• 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)

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.