AFD-INSTRUCTION: A Comprehensive Antibody Instruction Dataset with Functional Annotations for LLM-Based Understanding and Design

2026-02-04

AI Summary

This work addresses the limitation of current large language models in antibody understanding and design, which stems from a lack of high-quality instruction data aligning antibody sequences with functional descriptions. To bridge this gap, the authors construct AFD-INSTRUCTION, the first large-scale instruction dataset specifically tailored for antibodies, encompassing two core tasks: antibody function comprehension and constraint-based generation. This dataset enables explicit alignment between antibody sequences and natural language functional annotations for the first time. Through instruction tuning on AFD-INSTRUCTION, general-purpose large language models achieve substantial performance gains across diverse antibody-related tasks. The study thus establishes a novel paradigm and provides a foundational resource for natural language–guided antibody modeling and therapeutic antibody discovery.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) have significantly advanced protein representation learning. However, their capacity to interpret and design antibodies through natural language remains limited. To address this challenge, we present AFD-Instruction, the first large-scale instruction dataset with functional annotations tailored to antibodies. This dataset encompasses two key components: antibody understanding, which infers functional attributes directly from sequences, and antibody design, which enables de novo sequence generation under functional constraints. These components provide explicit sequence-function alignment and support antibody design guided by natural language instructions. Extensive instruction-tuning experiments on general-purpose LLMs demonstrate that AFD-Instruction consistently improves performance across diverse antibody-related tasks. By linking antibody sequences with textual descriptions of function, AFD-Instruction establishes a new foundation for advancing antibody modeling and accelerating therapeutic discovery.

Problem

• antibody
• large language models
• functional annotation
• instruction dataset
• sequence-function alignment

Innovation

• antibody instruction tuning
• sequence-function alignment
• large language models
• antibody design
• functional annotation