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 1–25 of 175 papers

TitleStatusHype
OpenProteinSet: Training data for structural biology at scaleCode4
Improved motif-scaffolding with SE(3) flow matchingCode3
Proteina: Scaling Flow-based Protein Structure Generative ModelsCode3
X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Molecular DesignCode3
A General Framework for Inference-time Scaling and Steering of Diffusion ModelsCode3
Robust deep learning based protein sequence design using ProteinMPNNCode3
TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence GenerationCode3
Geometry-Complete Diffusion for 3D Molecule Generation and OptimizationCode2
Hypergraph Isomorphism ComputationCode2
ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone GenerationCode2
ProGen2: Exploring the Boundaries of Protein Language ModelsCode2
RITA: a Study on Scaling Up Generative Protein Sequence ModelsCode2
MotifBench: A standardized protein design benchmark for motif-scaffolding problemsCode2
A Text-guided Protein Design FrameworkCode2
Concept Bottleneck Language Models For protein designCode2
Knowledge-Design: Pushing the Limit of Protein Design via Knowledge RefinementCode2
DiffDock-PP: Rigid Protein-Protein Docking with Diffusion ModelsCode2
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language ModelsCode2
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2Code2
Fast protein backbone generation with SE(3) flow matchingCode2
RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow MatchingCode2
Geometric Trajectory Diffusion ModelsCode1
Generative power of a protein language model trained on multiple sequence alignmentsCode1
Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue CloudsCode1
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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