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 51–100 of 175 papers

TitleStatusHype
ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design—0
Bridge-IF: Learning Inverse Protein Folding with Markov BridgesCode1
EMOCPD: Efficient Attention-based Models for Computational Protein Design Using Amino Acid Microenvironment—0
Peptide-GPT: Generative Design of Peptides using Generative Pre-trained Transformers and Bio-informatic SupervisionCode1
Training Free Guided Flow Matching with Optimal Control—0
MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Protein Language Models—0
Reinforcement learning on structure-conditioned categorical diffusion for protein inverse foldingCode1
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
Geometric Trajectory Diffusion ModelsCode1
What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs—0
Metalic: Meta-Learning In-Context with Protein Language ModelsCode1
Computational design of target-specific linear peptide binders with TransformerBeta—0
Plug-and-Play Controllable Generation for Discrete Masked Models—0
Towards deep learning sequence-structure co-generation for protein design—0
Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design—0
Recent advances in interpretable machine learning using structure-based protein representations—0
De novo design of high-affinity protein binders with AlphaProteo—0
ProteinBench: A Holistic Evaluation of Protein Foundation Models—0
Leveraging Deep Generative Model For Computational Protein Design And Optimization—0
BERT and LLMs-Based avGFP Brightness Prediction and Mutation Design—0
RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching—0
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein DesignCode1
Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design—0
Reinforcement Learning for Sequence Design Leveraging Protein Language Models—0
Fast uncovering of protein sequence diversity from structure—0
RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow MatchingCode2
UniIF: Unified Molecule Inverse Folding—0
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient—0
Learning the Language of Protein StructureCode1
Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2Code2
SurfPro: Functional Protein Design Based on Continuous Surface—0
Model-based reinforcement learning for protein backbone design—0
ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering—0
Annotation-guided Protein Design with Multi-Level Domain Alignment—0
Using GANs for De Novo Protein Design Targeting Microglial IL-3Rα to Inhibit Alzheimer's Progression—0
Diffusion on language model encodings for protein sequence generation—0
TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence GenerationCode3
Generative AI for Controllable Protein Sequence Design: A Survey—0
X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Molecular DesignCode3
Generative Adversarial Model-Based Optimization via Source Critic RegularizationCode0
Learning immune receptor representations with protein language models—0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learningCode1
Improved motif-scaffolding with SE(3) flow matchingCode3
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design—0
Progressive Multi-Modality Learning for Inverse Protein FoldingCode1
Fast non-autoregressive inverse folding with discrete diffusionCode1
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein DesignCode0
AI-predicted protein deformation encodes energy landscape—0
De novo protein design using geometric vector field networksCode1
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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