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
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learningCode1
Energy-based models for atomic-resolution protein conformationsCode1
Peptide-GPT: Generative Design of Peptides using Generative Pre-trained Transformers and Bio-informatic SupervisionCode1
Practical and Asymptotically Exact Conditional Sampling in Diffusion ModelsCode1
Metalic: Meta-Learning In-Context with Protein Language ModelsCode1
Learning from Protein Structure with Geometric Vector PerceptronsCode1
Progressive Multi-Modality Learning for Inverse Protein FoldingCode1
Improving large language models with concept-aware fine-tuningCode1
AlphaFold Distillation for Protein DesignCode1
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designCode1
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA DesignCode1
De novo protein design using geometric vector field networksCode1
Geometric Trajectory Diffusion ModelsCode1
Generative De Novo Protein Design with Global ContextCode1
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDBCode1
Diffusion Models for Constrained DomainsCode1
Improving few-shot learning-based protein engineering with evolutionary samplingCode1
Generative power of a protein language model trained on multiple sequence alignmentsCode1
Diffusion Sequence Models for Enhanced Protein Representation and GenerationCode1
Bridge-IF: Learning Inverse Protein Folding with Markov BridgesCode1
Learning the Language of Protein StructureCode1
Controllable Protein Sequence Generation with LLM Preference OptimizationCode1
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein DesignCode1
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein DesignCode1
Fast non-autoregressive inverse folding with discrete diffusionCode1
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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