Papers Protein Design
“Protein Design” 태그가 달린 논문 256편 · 필터 해제
Generative modeling of intrinsically disordered protein regions by reinforcing sparse autoencoder features
Intrinsically disordered protein regions (IDRs) play central roles in cellular processes such as transcriptional regulation, signal transduction, and subcellular localization, yet their functional design remains challeng…
Protein Language ModelReinforcement LearningProtein DesignCODesign: Consistency from Data to Trajectory in All-Atom Protein Binder Co-Design
The central challenge in de novo protein design is generating plausible, mutually compatible structures and sequences, such that each designed sequence folds into its intended structure and the structure accommodates tha…
Protein DesignProtein Structure Prediction: From Evolutionary Constraints to Generative Modeling
Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation. Recent advances in deep learning have…
Protein Structure PredictionMultiple Sequence AlignmentProtein DesignChamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling
The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via joint sequence-structure modeling or hallucin…
Protein DesignSciForge: An AI-Native, Multimodal Workbench for Scientific Discovery
Scientific work increasingly spans heterogeneous artifacts -- papers, code, datasets, scientific file formats, model outputs, figures, manuscripts, and team decisions -- yet general-purpose AI assistants rarely preserve …
Protein DesignVariable-Length Generative Protein Design via Generalized Poisson Flow
The ability to generate variable-length proteins is crucial in protein design, where the optimal length is often unknown and tightly coupled to designability. Current diffusion- and flow-based generative models typically…
Protein DesignDesign-CP: Context Parallelism for Design of Protein Nanoparticles
Many all-atom generative protein models can in principle design large multimeric complexes by jointly modelling all chains, but their quadratic token- and atom-pair representations quickly exceed single-GPU memory as the…
Protein DesignDiffeomorphic Optimization
Generative models learn data distributions that reside on a low-dimensional manifold within a higher-dimensional ambient space. Optimizing differentiable objectives on this manifold is challenging: the ambient loss lands…
Protein DesignTwo-Stage Fine-Tuning for Protein Sequence Generation with Targeted Amino-Acid Composition
Protein language models are standard priors for biological sequence generation, but steering them toward explicit distributional design targets remains largely unexplored. We study a constrained protein generation proble…
Reinforcement LearningProtein DesignFlexible Kernels for Protein Property Prediction
Despite its importance to applications in protein design, predicting protein properties like binding affinity and thermostability from sparse experimental data remains a significant challenge. Accordingly, we introduce a…
Multi-Task LearningGaussian ProcessesProtein DesignVFUSE: Virulent Feature Understanding with Sparse autoEncoders
Generative models have shown remarkable progress in a variety of domains such as protein design, but such power enables the opaque generation of hazardous proteins. In this work, we introduce VFUSE (Virulent Feature Unde…
Protein DesignDemystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling
Biomolecules such as proteins and small-molecule ligands play a central role in biological systems, arising from the tight interplay between sequence and three-dimensional structure. Recent generative models for biomolec…
Protein DesignSelf-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets
Protein sequence optimization under tight oracle budgets requires methods that explore vast combinatorial spaces while making each evaluation informative. Existing reinforcement learning and off-policy generative approac…
Reinforcement LearningProtein DesignCo-folding model guided by structural proteomics
Protein structure generative models excel at predicting single protein static structures from sequence, but routinely fail to capture the correct conformational state of protein complexes, critical for protein design and…
Protein DesignSurfDesign: Effective Protein Design on Molecular Surfaces
Protein function is largely determined by molecular surface geometry and physicochemical complementarity, yet most protein design methods condition only on backbone structure. We introduce SurfDesign, a surface-condition…
parameter-efficient fine-tuningProtein DesignEvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation
Equivariant graph neural network (GNN) methods for antibody complementarity-determining region (CDR) design achieve the highest sequence recovery but suffer from severe vocabulary collapse. The current best GNN methods o…
Protein Language ModelGraph Neural NetworkProtein DesignDCFold: Efficient Protein Structure Generation with Single Forward Pass
AlphaFold3 introduces a diffusion-based architecture that elevates protein structure prediction to all-atom resolution with improved accuracy. This state-of-the-art performance has established AlphaFold3 as a foundation …
Protein Structure PredictionProtein DesignLigand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design
Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design requires models that generate sequence-structure compatible proteins …
Protein DesignMultimodal Alignment and Preference Optimization for Zero-Shot Conditional RNA Generation
The design of RNA molecules that interact with specific proteins is a critical challenge in experimental and computational biology. Despite recent progress in natural language modeling and deep learning-based protein des…
Protein DesignProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Design
Designing proteins with desired functions or properties represents a core goal in synthetic biology and drug discovery. Recent advances in protein language models (PLMs) have enabled the generation of highly designable p…
Protein DesignDrug Discovery