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 101–125 of 175 papers

TitleStatusHype
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
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
UniIF: Unified Molecule Inverse Folding—0
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient—0
SurfPro: Functional Protein Design Based on Continuous Surface—0
Model-based reinforcement learning for protein backbone design—0
ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering—0
Annotation-guided Protein Design with Multi-Level Domain Alignment—0
Using GANs for De Novo Protein Design Targeting Microglial IL-3Rα to Inhibit Alzheimer's Progression—0
Diffusion on language model encodings for protein sequence generation—0
Generative AI for Controllable Protein Sequence Design: A Survey—0
Generative Adversarial Model-Based Optimization via Source Critic RegularizationCode0
Learning immune receptor representations with protein language models—0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design—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