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 51–75 of 175 papers

TitleStatusHype
ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design—0
Bridge-IF: Learning Inverse Protein Folding with Markov BridgesCode1
EMOCPD: Efficient Attention-based Models for Computational Protein Design Using Amino Acid Microenvironment—0
Peptide-GPT: Generative Design of Peptides using Generative Pre-trained Transformers and Bio-informatic SupervisionCode1
Training Free Guided Flow Matching with Optimal Control—0
MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Protein Language Models—0
Reinforcement learning on structure-conditioned categorical diffusion for protein inverse foldingCode1
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
Geometric Trajectory Diffusion ModelsCode1
What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs—0
Metalic: Meta-Learning In-Context with Protein Language ModelsCode1
Computational design of target-specific linear peptide binders with TransformerBeta—0
Plug-and-Play Controllable Generation for Discrete Masked Models—0
Towards deep learning sequence-structure co-generation for protein design—0
Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design—0
Recent advances in interpretable machine learning using structure-based protein representations—0
De novo design of high-affinity protein binders with AlphaProteo—0
ProteinBench: A Holistic Evaluation of Protein Foundation Models—0
Leveraging Deep Generative Model For Computational Protein Design And Optimization—0
BERT and LLMs-Based avGFP Brightness Prediction and Mutation Design—0
RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching—0
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein DesignCode1
Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design—0
Reinforcement Learning for Sequence Design Leveraging Protein Language Models—0
Fast uncovering of protein sequence diversity from structure—0
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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