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 76–100 of 175 papers

TitleStatusHype
A natural upper bound to the accuracy of predicting protein stability changes upon mutations—0
Applying computational protein design to therapeutic antibody discovery -- current state and perspectives—0
A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design—0
A Variational Perspective on Generative Protein Fitness Optimization—0
Backdiff: a diffusion model for generalized transferable protein backmapping—0
Benchmarking deep generative models for diverse antibody sequence design—0
BERT and LLMs-Based avGFP Brightness Prediction and Mutation Design—0
Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO—0
Breaking the Performance Ceiling in Complex Reinforcement Learning requires Inference Strategies—0
Clusters and Coarse Partitions in LP Relaxations—0
Computational design of target-specific linear peptide binders with TransformerBeta—0
Computational Protein Design Using AND/OR Branch-and-Bound Search—0
Computational Protein Design with Deep Learning Neural Networks—0
Computational Protein Science in the Era of Large Language Models (LLMs)—0
Conditional Generative Modeling for De Novo Hierarchical Multi-Label Functional Protein Design—0
Deep Generative Modeling for Protein Design—0
De novo design of high-affinity protein binders with AlphaProteo—0
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning—0
Design in the Dark: Learning Deep Generative Models for De Novo Protein Design—0
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action—0
Diffusion on language model encodings for protein sequence generation—0
DockGame: Cooperative Games for Multimeric Rigid Protein Docking—0
DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design—0
Efficient generative modeling of protein sequences using simple autoregressive models—0
EMOCPD: Efficient Attention-based Models for Computational Protein Design Using Amino Acid Microenvironment—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