diffusion model

A Whole-Mouse-Brain Model of Coupled Neural Masses: Analysis of Network Dynamics

The paper analyzes the dynamics of a whole-mouse-brain neural mass model coupled via an empirical connectome, finding chaotic oscillations and suppression of chaos with network modifications.

Diffusion-Based Synthetic Pretraining for Root Segmentation in Rhizotron Images

This paper generates synthetic rhizotron images using diffusion models and shows that pretraining on these images improves root segmentation performance, especially when real annotations are limited.

SMFnet reveals principles of combinatorial transcription factor assembly

The paper introduces SMFNet and PRISM to analyze how transcription factor assemblies influence chromatin accessibility and identifies sequence-encoded principles of combinatorial TF assembly.

IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder

IDPForge generates all-atom ensembles of intrinsically disordered proteins and regions using a transformer diffusion model, showing good agreement with experimental data.

Predicting conformational changes induced by biomolecular interactions

This paper introduces K-Fold, a method that improves prediction of conformational changes in protein complexes by modeling structural transitions from unbound to bound states.

DAPE-PD: Diffusion adaptation of genomic foundation model enables tailored-generation of cis-regulatory DNA

DAPE-PD adapts a genomic foundation model into a diffusion model to generate functional cis-regulatory DNA sequences across three non-model gut commensals.

Accelerated discovery and optimization of a potent andselective MMP12 inhibitor with the machine learningplatform CAMOLE-1

This paper describes the development of CAMOLE-1, a machine learning platform for designing potent and selective MMP12 peptide inhibitors, achieving results in about one year with 270 synthesized peptides.

RiboDFM: Robust RNA sequence design using backbone-conditioned discrete flow matching

RiboDFM designs RNA sequences that fold into target 3D structures using a backbone-conditioned discrete flow-matching framework, achieving high sequence recovery and tertiary-structure consistency.

MYC targeted de novo binders impair tumor growth

Designed protein binders to conserved MYC regions reach low-nanomolar affinity, reduce MYC-MAX proximity in cells, and are depleted from MYC-dependent cancer cells in culture and mouse pancreatic tumors.

Complex enzyme active site scaffolding by iteratively detuned catalytic guidance

CaGE alternates guided structure prediction with sequence redesign, fading guidance each cycle, so sequences recover complex catalytic sites unaided; designed nonheme-iron and PLP-dependent enzymes were active.

Protein Deimmunization with a Generative Diffusion Model

The paper introduces MESSI, a generative diffusion model for deimmunizing proteins by designing sequences with minimal immunogenicity while preserving structure and function.

DiffuChannel: A Conditional Denoising Diffusion Model that Synthesises Single-Molecule Patch-Clamp Data with Exactly Known Idealisation (Ground Truth)

DiffuChannel generates synthetic single-molecule patch-clamp data with known idealisation using a conditional diffusion model, outperforming GAN-based methods in stability and realism.

Computational design of metalloproteases

This paper designs de novo metalloproteases using RoseTTAFold Diffusion 2, achieving high activity and specificity for cleaving various substrates.

Pocket-Conditioned Generation and Down-Selection of Non-Oxime Reactivators

This paper compares generative models for designing non-oxime reactivators of paraoxon-inhibited acetylcholinesterase, finding that down-selection methods outperform DrugDiff in practical drug-likeness and synthesis feasibility.

Large scale prospective evaluation of co-folding across hundreds of Mac1-ligand complexes and three virtual screens

On 489 evaluable new Mac1 complexes, AlphaFold3, Chai-1 and Boltz-2 placed over half of ligands within 2 Å, but on docking hit-lists AF3 and Boltz-2 added little over docking.

AlphaFold3 and Intrinsically Disordered Proteins: Reliable Monomer Prediction, Unpredictable Multimer Performance

AlphaFold3 pLDDT matches AlphaFold2 at calling disordered residues (Matthews correlation 0.693 vs 0.701); on disorder-containing complexes mean DockQ is similar, but interface features explain less variance (42% vs 58%).

A dynamical attractor computes reward prediction in the basal forebrain

The paper shows that basal forebrain bursting is generated by a dynamical attractor that preserves burst structure while integrating inputs into a scalar amplitude encoding reward prediction.