diffusion model

Generative machine learning unlocks the first proteome-wide image of human cells

A conditional latent diffusion transformer predicts immunofluorescence images of 12,800 human proteins from nucleus, ER and microtubule stains, outperforming PUPS and CELL-Diff, including on an unseen drug-treated cell line.

siProGenA: Generative siRNA Candidate Construction via Position Proposal and Guide Generation

siProGenA generates siRNA candidates by proposing positions and generating guide sequences, achieving high Hit@1 and Hit@5 scores in controlled evaluations.

GNMCADS: Sampling For Protein Conformation Diversity With Gaussian Network Model Guided Condition Annealed Diffusion Sampler

GNMCADS improves conformational sampling diversity by guiding diffusion models with Gaussian Network Model dynamics, outperforming existing methods on 92 proteins.

Designing antimicrobials with programmable mechanism and safety

OmegAMP generates antimicrobial peptides with controlled physicochemical properties and safety profiles, showing activity against drug-resistant bacteria in vitro and in vivo.

Privacy-Preserving Visualization of Brain Functional Connectivity

This paper explores privacy-preserving visualization techniques for brain functional connectivity using differential privacy to protect participant data while maintaining visual utility.

Exploring protein conformational ensembles using evolutionary conditional diffusion

This paper introduces DiffEnsemble, a diffusion-based framework for modeling protein conformational ensembles, showing improved performance over existing methods.

GDA-Pred: Generative AI-Driven Data Augmentation for Improved Prediction of IL-6 and IL-13 Inducing Peptides

GDA-Pred improves prediction of IL-6 and IL-13 inducing peptides by using generative AI to augment small datasets, enhancing classifier performance through data expansion.

Enhanced sampling of protein conformations in AlphaFold3 with repulsive bias in the diffusion generative model

This paper improves AlphaFold3's ability to predict protein conformations by introducing a repulsive bias in its diffusion model, enabling better capture of ligand-induced structural changes.

UTR-Diffusion: Conditional Diffusion Modeling for Multi-objective and Constrained UTR Design

UTR-Diffusion is a diffusion model for designing 5-prime UTR and UTR-CDS junctions that optimizes translation efficiency, RNA stability, and codon adaptation while preserving sequence constraints.

Time-dependent bistability leads to critical slowing down during floral transition in Arabidopsis

The paper shows time-dependent bistability in Arabidopsis floral transition, leading to critical slowing down and ghost attractor dynamics.

bulk2scDiff: A Pseudobulk-Conditioned Diffusion Model for Bulk-to-Single-Cell RNASeq Generation

bulk2scDiff generates single-cell RNA-seq data from bulk profiles using a diffusion model, showing good reconstruction of cell populations in cancer datasets.

Physically Grounded Generative Modeling of All-Atom Biomolecular Dynamics

BioKinema generates physically accurate all-atom biomolecular trajectories at lower computational cost than traditional MD simulations, enabling dynamic analysis of protein and protein-ligand systems.

The Synthetic Fidelity-Stability Framework (SFSF): A Systematic Multi-Dimensional Benchmark of Synthetic Clinical Laboratory Data Generators

The paper benchmarks 17 synthetic data generators for clinical lab data, finding no single model excels across all metrics, and highlights ForestDiffusion's best privacy-utility balance.

Brain2VLM: Hierarchical Alignment Between Cortical Representations and Vision-Language Latent Spaces

A residual MLP beats ridge by Δ≈0.47 correlation decoding CLIP embeddings from higher visual areas, but only Δ≈0.05 to 0.06 decoding Stable Diffusion latents from early visual cortex.

siDiff: De Novo siRNA Design via Efficacy-Guided Discrete Masked Diffusion

siDiff improves siRNA design by combining a diffusion model with an efficacy guidance model, achieving better hit rates and generalization across target genes.

Biasing Conformational Sampling in AlphaFold 3 and Boltz-2 via Pair Representation Scaling

Multiplying the AlphaFold 3 or Boltz-2 pair representation by a single scalar (1+β) at the Pairformer input, without retraining, broadens sampled ensembles toward alternative states across 86 two-state targets.

Unified Sampling and Ranking for Protein Docking with DFMDock

DFMDock is a diffusion model for protein docking that combines generative sampling and energy-based ranking, achieving higher success rates than existing methods.

Boltz-Perturb: Improving Diversity and Accuracy in Protein-Ligand Co-Folding through Training-Free Conditioning Perturbation

Boltz-Perturb improves ligand binding pose prediction by introducing inference-time perturbations, achieving higher success rates with less computation.

Hoike: A Joint-Embedding Predictive Architecture for Transcriptome Data Generation with Diffusion Models

Hoike generates condition-specific bulk transcriptomes using a joint-embedding predictive architecture and diffusion models, conditioned on normal tissue profiles.

Kukulu: Diffusion-Based Reconstruction of Antibody CDR Loops using a Structure-Aware Joint Embedding Predictive Architecture

Kukulu reconstructs antibody CDR loops using a diffusion model with a structure-aware architecture, improving loop design by integrating sequence-structure constraints.

Seed-Guided De Novo Design Expands the Structural Diversity of Antitoxin Protein Binders

This paper introduces seed-guided diffusion-based design to improve structural diversity and target contact in antitoxin protein binders, achieving functional binders with enhanced selectivity.

Beyond Generic Signal Peptides: ApexSP Enables Cargo-Specific Secretion Design

ApexSP designs cargo-specific signal peptides for efficient secretion by integrating evolutionary information, compatibility modeling, and experimental validation across multiple expression systems.

Diffusion models learn underlying trends in actomyosin networks and predict behavior at unseen filament turnover

The paper trains a diffusion model on actomyosin system data to predict nonlinear trends in cortical flow at unseen filament turnover rates.

Pseudotime graph diffusion for post hoc visualization of inferred single-cell trajectories

PGD improves visualization of single-cell trajectories by smoothing cell-level features along pseudotime, enhancing continuity and structure in complex biological processes.

Targeting Ligand Shape While Preserving a Warhead through Guided Diffusion and Adaptive Staged Growth

This paper improves molecular growth models by using shape guidance and adaptive growth to connect fragments while preserving a warhead.

Programming protein shape as an explicit design layer via CAD blueprint-guided diffusion

The paper integrates CAD blueprints with diffusion models to explicitly program protein shapes, demonstrating emergent functions through molecular design.

Virome-mediated ecological firewalls govern pathogen eradication in the gut microbiome

The paper introduces a dynamical model to study how gut virome influences microbiome recovery after antibiotic-induced dysbiosis, identifying ecological thresholds and a firewall mechanism for pathogen suppression.

De Novo Design of Protein Switches with Diffusion-Based Ensemble Sampling

This paper introduces Diff-Switch, a method to generate switch-like protein backbone ensembles using diffusion models, improving success rates in multi-state inverse folding.

ProteinSketch translates spatial intuition into protein design with bimanual interaction in VR

ProteinSketch enables interactive protein design in VR by translating spatial intuition into constraints for AI-driven protein structure generation.

A Glycan-Aware Diffusion Model for Carbohydrate and Glycoprotein Structure Prediction

This paper introduces SweetFold, a diffusion model for predicting glycan and glycoprotein structures, showing improved performance over existing models.