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 1–10 of 175 papers

TitleStatusHype
OpenProteinSet: Training data for structural biology at scaleCode4
Proteina: Scaling Flow-based Protein Structure Generative ModelsCode3
Robust deep learning based protein sequence design using ProteinMPNNCode3
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
Improved motif-scaffolding with SE(3) flow matchingCode3
TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence GenerationCode3
Concept Bottleneck Language Models For protein designCode2
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
Geometry-Complete Diffusion for 3D Molecule Generation and OptimizationCode2
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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