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 126–150 of 175 papers

TitleStatusHype
Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue CloudsCode1
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA DesignCode1
Protein Structure Prediction until CASP15—0
AlphaFold Distillation for Protein DesignCode1
PiFold: Toward effective and efficient protein inverse foldingCode1
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
ProGen2: Exploring the Boundaries of Protein Language ModelsCode2
Robust deep learning based protein sequence design using ProteinMPNNCode3
RITA: a Study on Scaling Up Generative Protein Sequence ModelsCode2
TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs—0
Generative De Novo Protein Design with Global ContextCode1
Generative power of a protein language model trained on multiple sequence alignmentsCode1
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDBCode1
Gate-based Quantum Computing for Protein DesignCode0
Controllable Protein Design with Language Models—0
Benchmarking deep generative models for diverse antibody sequence design—0
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designCode1
Fast fixed-backbone protein sequence and rotamer design—0
Design in the Dark: Learning Deep Generative Models for De Novo Protein Design—0
PDBench: Evaluating Computational Methods for Protein Sequence Design—0
Deep Generative Modeling for Protein Design—0
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein DesignCode1
ProDCoNN-server: a web server for protein sequence prediction and design from a three-dimensional structureCode0
Efficient generative modeling of protein sequences using simple autoregressive models—0
Mimetic Neural Networks: A unified framework for Protein Design and Folding—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