diffusion model

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.

Computational design of metalloproteases

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

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.

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.

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.

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.

Robust Conditional Diffusion with Noisy Templates for Antibody Sequence-Structure Design

This paper introduces NT-ABDiff, a diffusion model for antibody sequence-structure design that improves CDR recovery and structural accuracy using noisy and unreliable templates.

Fast and Ultra-Capable Protein Design: Advancing the Frontier Through Atomistic SE(3)-Equivariance with Genie 3

This paper introduces Genie 3, a diffusion model that improves protein design by combining SE(3)-equivariance with hallucination, achieving state-of-the-art results in binder design and motif scaffolding.

Computational Design and Atomistic Validation of a High-Affinity VHH Nanobody Targeting the PI/RuvC Interface of Streptococcus pyogenes Cas9: A Bivalent Hub Strategy for CRISPR-Cas9 Enhancement

The paper computationally designs a VHH nanobody targeting Cas9's PI/RuvC interface and validates its structure and stability through molecular dynamics simulations.

Designing protein/non-protein binding interactions using a full-atom diffusion model

The paper introduces AtomPaint, a full-atom diffusion model for designing protein/non-protein binding interactions, showing it outperforms random guessing in sequence recovery.

Designing AAV Capsid Protein with viability-guided Diffusion Model

This paper introduces AAVDiffusion, a viability-guided diffusion model for designing viable AAV capsid proteins, showing improved performance in generating functional sequences.

Improved protein binder design using -pairing targeted RFdiffusion

Conditioning RFdiffusion on edge beta-strand pairing yields specific binders to polar targets, with in silico success above 9.2% versus 0.98% for hotspot-only conditioning.

Computational enzyme design by catalytic motif scaffolding

This paper introduces Riff-Diff, a machine learning and atomistic modeling method for designing enzymes with high catalytic activity and stereoselectivity, demonstrating its effectiveness for two chemical reactions.

Atom level enzyme active site scaffolding using RFdiffusion2

RFdiffusion2 scaffolds unindexed atom-level catalytic active sites via flow matching without inverse rotamer enumeration, solving all 41 in silico benchmark cases and yielding active de novo enzymes across four reactions.

Computational Design of Metallohydrolases

This paper designs highly active metallohydrolases using a new generative AI method, achieving kcat/KM values up to 53,000 M-1 s-1 without experimental optimization.

All-Atom Protein Sequence Design using Discrete Diffusion Models

This paper introduces a discrete diffusion model for protein sequence design using all-atom representations, showing improved novelty and diversity compared to amino acid-based models.

BoltzGen: Toward Universal Binder Design

An all-atom diffusion model unifying biomolecular structure prediction and sequence-structure generation designs experimentally validated nanobodies, mini-proteins, and linear or cyclic peptides across 26 diverse targets.

SpecLig: Energy-Guided Hierarchical Model for Target-Specific 3D Ligand Design

SpecLig generates target-specific ligands with high affinity and specificity by integrating hierarchical modeling and energy guidance, improving therapeutic potential and reducing off-target risks.

Bottom-up design of calcium channels from defined selectivity filter geometry

This paper designs Ca2+ channels with specific selectivity filter geometries using a bottom-up approach, showing higher Ca2+ conductance and structural accuracy.

All-atom protein design via SE(3) flow matching with ProteinZen

This paper introduces ProteinZen, a method for all-atom protein design using SE(3) flow matching, achieving high sequence-structure consistency and competitive performance in design tasks.

Constrained Diffusion for Protein Design with Hard Structural Constraints

This paper introduces a constrained diffusion framework for protein design that enforces strict structural constraints while maintaining structural diversity, achieving state-of-the-art results on motif scaffolding and pocket design tasks.

Leveraging protein representations to explore uncharted fold spaces with generative models

This paper introduces DiffTopo, a diffusion model for generating novel protein folds, and MirrorTopo, a pipeline to expand known fold space by mirroring native protein topologies.

Protein Hunter: exploiting structure hallucination within diffusion for protein design

Protein Hunter uses diffusion models to design proteins de novo by iteratively improving sequence and structure through hallucination and re-design.

FlexRibbon: Joint Sequence and Structure Pretraining for Protein Modeling

FlexRibbon is a pretrained protein model that jointly learns from sequences and structures, achieving state-of-the-art performance in mutation-rich settings.

Peptide Design through Binding Interface Mimicry

PepMimic, an all-atom latent diffusion model guided by a contrastive interface encoder, designs peptides mimicking known binders; 8% of tested peptides across five targets reached 10^-8M KD.

De novo design of RNA and nucleoprotein complexes

This paper introduces a generative diffusion model for de novo design of RNA and nucleoprotein complexes, validated experimentally with SHAPE-seq and electron microscopy.