SOTAVerified

Protein Design

Formally, given the design requirements of users, models are required to generate protein amino acid sequences that align with those requirements.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 175 papers

TitleStatusHype
Energy-based models for atomic-resolution protein conformationsCode1
Metalic: Meta-Learning In-Context with Protein Language ModelsCode1
Improving large language models with concept-aware fine-tuningCode1
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designCode1
Bridge-IF: Learning Inverse Protein Folding with Markov BridgesCode1
Generative power of a protein language model trained on multiple sequence alignmentsCode1
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA DesignCode1
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learningCode1
AlphaFold Distillation for Protein DesignCode1
Protein Design with Guided Discrete DiffusionCode1
De novo protein design using geometric vector field networksCode1
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein DesignCode1
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDBCode1
Practical and Asymptotically Exact Conditional Sampling in Diffusion ModelsCode1
PiFold: Toward effective and efficient protein inverse foldingCode1
Diffusion Models for Constrained DomainsCode1
Generative De Novo Protein Design with Global ContextCode1
Controllable Protein Sequence Generation with LLM Preference OptimizationCode1
Diffusion Sequence Models for Enhanced Protein Representation and GenerationCode1
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein DesignCode1
Geometric Trajectory Diffusion ModelsCode1
Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue CloudsCode1
Fast non-autoregressive inverse folding with discrete diffusionCode1
Improving few-shot learning-based protein engineering with evolutionary samplingCode1
Peptide-GPT: Generative Design of Peptides using Generative Pre-trained Transformers and Bio-informatic SupervisionCode1
Show:102550
← PrevPage 2 of 7Next →
All datasetsCATH 4.2CATH 4.3

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GraphTransPerplexity6.63—Unverified
2StructGNNPerplexity6.4—Unverified
3AlphaDesignPerplexity6.3—Unverified
4GCAPerplexity6.05—Unverified
5GVPPerplexity5.36—Unverified
6ProteinMPNNPerplexity4.61—Unverified
7PiFoldPerplexity4.55—Unverified
8Knowledge-DesignPerplexity3.46—Unverified
#ModelMetricClaimedVerifiedStatus
1ESM-IFPerplexity6.44—Unverified
2GVP-largePerplexity6.17—Unverified