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
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein DesignCode0
AI-predicted protein deformation encodes energy landscape—0
Generative artificial intelligence for de novo protein design—0
DockGame: Cooperative Games for Multimeric Rigid Protein Docking—0
Expected flow networks in stochastic environments and two-player zero-sum gamesCode0
Backdiff: a diffusion model for generalized transferable protein backmapping—0
Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO—0
Metropolis Sampling for Constrained Diffusion Models—0
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces—0
Robust Model-Based Optimization for Challenging Fitness LandscapesCode0
Scalable Coupling of Deep Learning with Logical ReasoningCode0
CCPL: Cross-modal Contrastive Protein Learning—0
Protein Sequence Design with Batch Bayesian OptimisationCode0
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action—0
Protein Structure Prediction until CASP15—0
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs—0
Gate-based Quantum Computing for Protein DesignCode0
Controllable Protein Design with Language Models—0
Benchmarking deep generative models for diverse antibody sequence design—0
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
ProDCoNN-server: a web server for protein sequence prediction and design from a three-dimensional structureCode0
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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