mlsanityData to 7 Oct 2026

A Text-guided Protein Design Framework

arXiv 2302.04611 ↗First posted 9 Feb 2023cs.LG cs.AI q-bio.QMcited by 61

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Abstract (arXiv)

Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in the text format describing proteins' high-level functionalities. Yet, whether the incorporation of such text data can help protein design tasks has not been explored. To bridge this gap, we propose ProteinDT, a multi-modal framework that leverages textual descriptions for protein design. ProteinDT consists of three subsequent steps: ProteinCLAP which aligns the representation of two modalities, a facilitator that generates the protein representation from the text modality, and a decoder that creates the protein sequences from the representation. To train ProteinDT, we construct a large dataset, SwissProtCLAP, with 441K text and protein pairs. We quantitatively verify the effectiveness of ProteinDT on three challenging tasks: (1) over 90% accuracy for text-guided protein generation; (2) best hit ratio on 12 zero-shot text-guided protein editing tasks; (3) superior performance on four out of six protein property prediction benchmarks.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). Builds on 57 of its 149 references that are in the map. First posted on arXiv 9 Feb 2023.

Builds on

57 of 149 references.

References · 57

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50 most cited of 57 All 57 (JSON)

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Citing papers per year: 2023: 13; 2024: 25; 2025: 15; 2026: 8.

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  1. 2023Protein Design with Guided Discrete Diffusioncited by 154
  2. 2023BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associationscited by 100
  3. 2024A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discoverycited by 96
  4. 2024Scientific Large Language Models: A Survey on Biological & Chemical Domainscited by 80
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  6. 2025From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discoverycited by 74
  7. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  8. 2024BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuningcited by 54
  9. 2024Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Spacecited by 33
  10. 2023A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretrainingcited by 32
  11. 2023Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformerscited by 32
  12. 2024ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedbackcited by 32
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  2. 2024Data-Centric Foundation Models in Computational Healthcare: A Surveycited by 27
  3. 2024ProtT3: Protein-to-Text Generation for Text-based Protein Understandingcited by 25
  4. 2023BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphscited by 24
  5. 2024Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trialscited by 24
  6. 2023Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materialscited by 22
  7. 2025Protein Large Language Models: A Comprehensive Surveycited by 18
  8. 2024Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generationcited by 17
  9. 2023Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Designcited by 16
  10. 2024Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Surveycited by 16
  11. 2025Multimodal Large Language Models for Medicine: A Comprehensive Surveycited by 16
  12. 2024PAPERCLIP: Associating Astronomical Observations and Natural Language with Multi-Modal Modelscited by 15
  13. 2023A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generationcited by 10
  14. 2024Advancing biomolecular understanding and design following human instructionscited by 10
  15. 2024Annotation-guided Protein Design with Multi-Level Domain Alignmentcited by 9
  16. 2024Generative AI for Controllable Protein Sequence Design: A Surveycited by 8
  17. 2024CARE: a Benchmark Suite for the Classification and Retrieval of Enzymescited by 7
  18. 2024ProtCLIP: Function-Informed Protein Multi-Modal Learningcited by 6
  19. 2025Computational Protein Science in the Era of Large Language Models (LLMs)cited by 6
  20. 2023Generative artificial intelligence for de novo protein designcited by 5
  21. 2025From large language models to multimodal AI: A scoping review on the potential of generative AI in medicinecited by 5
  22. 2025Protein Design with Dynamic Protein Vocabularycited by 5
  23. 2025PDFBench: A Benchmark for De novo Protein Design from Functioncited by 5
  24. 2023Defining a New NLP Playgroundcited by 4
  25. 2024Bridging Text and Molecule: A Survey on Multimodal Frameworks for Moleculecited by 4
  26. 2024Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding with LLMscited by 4
  27. 2025Rethinking Text-based Protein Understanding: Retrieval or LLM?cited by 4
  28. 2024Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimationcited by 3
  29. 2024Function-Guided Conditional Generation Using Protein Language Models with Adapterscited by 3
  30. 2025TEMPO: Temporal Multi-scale Autoregressive Generation of Protein Conformational Ensemblescited by 3
  31. 2023Computational modeling of semantic changecited by 2
  32. 2026Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inferencecited by 2
  33. 2023Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirementscited by 1
  34. 2025InstructPro: Natural Language Guided Ligand-Binding Protein Designcited by 1
  35. 2025Expert-Guided LLM Reasoning for Battery Discovery: From AI-Driven Hypothesis to Synthesis and Characterizationcited by 1
  36. 2025MoRA: On-the-fly Molecule-aware Low-Rank Adaptation Framework for LLM-based Multi-Modal Molecular Assistantcited by 1
  37. 2026Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standardscited by 1
  38. 2026ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Designcited by 1

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