arXiv Machine Learning
Jul 2
Diffeomorphic Optimization
arXiv:2607. 00947v1 Announce Type: new Abstract: Generative models learn data distributions that reside on a low-dimensional manifold within a higher-dimensional ambient space.
SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups
arXiv:2607. 27431v1 Announce Type: new Abstract: Generative modeling of protein backbones promises the de novo design of proteins with prescribed structural and functional properties.
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arXiv AI
Jun 19
Emyx: Fast and efficient all-atom protein generation
arXiv:2606. 19377v1 Announce Type: cross Abstract: Computational enzyme design requires generating proteins that scaffold catalytic residues and ligands, a task that demands both geometric accuracy and structural diversity from the underlying generative model.
arXiv Machine Learning
Jul 2
PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks
arXiv:2504. 02839v2 Announce Type: replace-cross Abstract: Proteins move and deform to ensure their biological functions.
arXiv Machine Learning
Jun 9
Few-step Cofolding with All-Atom Flow Maps
arXiv:2606. 08375v1 Announce Type: new Abstract: All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems.
arXiv Machine Learning
Jun 11
ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation
arXiv:2606. 11243v1 Announce Type: new Abstract: De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology.
arXiv Machine Learning
Aug 4
Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery
arXiv:2509. 26405v2 Announce Type: replace Abstract: We introduce InVirtuoGen, a discrete flow generative model for fragmented SMILES for de novo and fragment-constrained generation, and target-property/lead optimization of small molecules.
arXiv Machine Learning
Jun 18
Riemannian MeanFlow for One-Step Generation on Manifolds
arXiv:2603. 10718v3 Announce Type: replace Abstract: Flow Matching enables simulation-free training of generative models on Riemannian manifolds, yet sampling typically still relies on numerically integrating a probability-flow ODE.
arXiv Machine Learning
Jul 13
Variable-Length Generative Protein Design via Generalized Poisson Flow
arXiv:2607. 09039v1 Announce Type: new Abstract: The ability to generate variable-length proteins is crucial in protein design, where the optimal length is often unknown and tightly coupled to designability.
arXiv Machine Learning
Jul 7
Stable Global Weighting of Flow Mixtures using Simplex Exponential Moving Average
arXiv:2607. 03809v1 Announce Type: new Abstract: Normalising flows provide a powerful variational family for approximate inference, yet individual architectures often fail to generalise across heterogeneous posterior geometries.
arXiv AI
Jun 2
Learning Implicit Bias in Generative Spaces for Accelerating Protein Dynamics Emulation
arXiv:2606. 01833v1 Announce Type: cross Abstract: Generative emulators of protein dynamics produce plausible trajectories at a fraction of the cost of molecular dynamics, but they inherit their training distribution and tend to revisit known states rather than reach rare ones under long-horizon extrapolation.
arXiv AI
Jun 9
AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model
arXiv:2507. 08920v4 Announce Type: replace-cross Abstract: We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, encompassing pretraining scaling laws, emergent capability analysis, in-context learning mechanism, and test-time scaling algorithm.
arXiv Machine Learning
Jun 16
Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions
arXiv:2511. 09465v4 Announce Type: replace-cross Abstract: Diffusion and flow matching approaches to generative modeling have shown promise in domains where the state space is continuous, such as image generation or protein folding & design, and discrete, exemplified by diffusion large language models.