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 21–30 of 175 papers

TitleStatusHype
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language ModelsCode2
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
Improving large language models with concept-aware fine-tuningCode1
Improving few-shot learning-based protein engineering with evolutionary samplingCode1
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designCode1
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA DesignCode1
Generative power of a protein language model trained on multiple sequence alignmentsCode1
Geometric Trajectory Diffusion ModelsCode1
Generative De Novo Protein Design with Global ContextCode1
Bridge-IF: Learning Inverse Protein Folding with Markov BridgesCode1
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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