diffusion model

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.

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.

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.

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.

AC-Diff: Anatomy-Contrast Disentangled Diffusion for Multi-Vendor Liver MRI Harmonization

AC-Diff improves liver MRI harmonization by separating anatomy from contrast, achieving better segmentation results.

MIMOSA: Guiding De Novo Antibody Design with Natural Interaction Fingerprints

MIMOSA improves de novo antibody design by constraining generative models to known binder interfaces, achieving validated binders on targets where unconstrained methods fail.

DesignMaster: A Multi-Conditional Diffusion Framework for Rational PROTAC Design

DesignMaster improves PROTAC design by incorporating linker length and physicochemical constraints through a diffusion framework, achieving better validity and recovery than existing methods.

A diffusion model of viral evolution predicts mutation fitness and evolutionary trajectories

VES predicts viral mutation fitness and evolutionary trajectories using a diffusion model trained on protein sequences, outperforming existing models in mutation fitting and forecasting future mutations.

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.

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.

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.

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.

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.

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.

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.

Generative design of antigen-specific T-cell receptor sequences with a conditional diffusion model

This paper introduces TCRDiff, a conditional diffusion model for generating antigen-specific TCR sequences, validated through in vitro assays.

Pep2Mol: 3D Molecule Generation Targeting Protein-Protein Interfaces with Diffusion Models

Pep2Mol generates 3D molecules targeting protein-protein interfaces using diffusion models, achieving state-of-the-art binding affinities for small-molecule inhibitors.

F.A.D.E. (Fully Agentic Drug Engine): A Conversational AI Platform for Drug Discovery

F.A.D.E. is a conversational AI platform that generates drug candidates from natural language queries, outperforming reference ligands in drug-likeness for two targets.

AlphaFold3 for Structure-guided Ligand Discovery

Retrospective and prospective benchmarks show AlphaFold3 co-folding memorizes training poses and achieves half the prospective hit rate of DOCK3 at 6,700-fold higher cost, but excels as a post-docking filter.

An Agentic Platform for Drug Repurposing Unified across Molecular, Phenotypic, and Clinical Scales

LinkD is a drug repurposing platform that integrates molecular, phenotypic, and clinical data to prioritize drugs, showing reduced prostate cancer incidence with beta-blockers.

Transcriptomics-Conditioned Virtual Tissue Synthesis via Diffusion Transformers

The paper introduces STMDiT, a diffusion transformer that synthesizes H&E histopathology images from transcriptomic data, improving image quality and transcriptomic fidelity over existing methods.

High-PepBinder: A pLM-Guided Latent Diffusion Framework for Affinity-Aware Target-Specific Peptide Design

High-PepBinder generates target-specific peptides using a pLM-guided diffusion model and a comprehensive dataset, producing peptides with favorable binding properties and sequence diversity.

X-Cell: Scaling Causal Perturbation Prediction Across Diverse Cellular Contexts via Diffusion Language Models

X-Cell predicts causal perturbation responses across diverse cellular contexts using a diffusion language model trained on a large CRISPRi Perturb-seq dataset.

Single-Pass Discrete Diffusion Predicts High-Affinity Peptide Binders at >1,000 Sequences per Second across 150 Receptor Targets

LigandForge generates high-affinity peptide binders rapidly across multiple receptor targets using a discrete diffusion model, outperforming existing methods in speed and binding quality.

Automated high-throughput fabrication of patient-specific vessel-on-chips enables a generative AI digital twin--Cascade Learner of Thrombosis (CLoT) for personalized thrombosis prediction

The paper develops a platform for automated vessel-on-chip fabrication and a generative AI model, CLoT, to predict thrombosis in patients with high accuracy.

Ophiuchus-Ab: A Versatile Generative Foundation Model for Advanced Antibody-Based Immunotherapy

Ophiuchus-Ab is a generative foundation model for antibody design that captures inter-chain dependencies and predicts antibody properties like developability and binding affinity.

Modelling Immune Dynamics in Locally Advanced MSI-H/dMMR Colorectal Cancer with Neoadjuvant Pembrolizumab Treatment: From Differential Equations to an Agent-Based Framework

This paper develops a differential equation model and converts it to an agent-based model to simulate immune dynamics in MSI-H/dMMR colorectal cancer under pembrolizumab treatment.

Reprogramming BCMA-Targeted CAR-T Cells through γ-Secretase Modulation Blocks Antigen Shedding and Extends CAR-T Longevity

This paper develops SHEDLOCK CAR-T cells that block BCMA shedding, enhancing their cytotoxicity and longevity in multiple myeloma models.