鲨鱼抗体数据集

针对生物医药领域中靶点药物筛选效率低、候选分子资源匮乏、AI 模型训练数据不足等核心痛点，依托超算算力与前沿生物技术，通过鲨鱼活体免疫、淋巴细胞分离、二代高通量测序等一系列严谨实验流程，构建起鲨鱼纳米抗体百万级数据库。该数据库涵盖百万级以上高质量纳米抗体序列，配套包含完整的实验溯源信息、分子特征标注与结构模拟数据，覆盖多种抗原免疫背景下的纳米抗体资源，可直接支撑药物筛选、分子性质预测、三维结构解析等关键研发环节，更能作为 AI 大模型训练的核心数据底座，助力 AI 智能计算驱动的创新药物研发，为突破生物医药领域的技术瓶颈提供坚实的数据支撑。

Targeting the core pain points in the field of biomedicine, including low efficiency of target drug screening, shortage of candidate molecular resources, and insufficient training data for AI models, we constructed a million-scale shark nanobody database through a series of rigorous experimental procedures such as in vivo shark immunization, lymphocyte separation, and second-generation high-throughput sequencing, with the support of supercomputing power and cutting-edge biotechnologies. This database contains over one million high-quality nanobody sequences, and is accompanied by complete experimental traceability information, molecular feature annotations and structural simulation data. It covers nanobody resources under various antigen immunization backgrounds, and can directly support key R&D links including drug screening, molecular property prediction, and three-dimensional structure elucidation. Moreover, it can serve as a core data foundation for training large language models (LLMs), enabling innovative drug research and development driven by AI intelligent computing, thereby providing solid data support for breaking through technical bottlenecks in the biomedicine field.

搜集汇总

背景与挑战

背景概述

该数据集旨在解决生物医药领域靶点药物筛选效率低和AI模型训练数据不足等问题，通过鲨鱼活体免疫和二代高通量测序等实验流程，构建了包含百万级以上高质量纳米抗体序列的数据库。数据集配套完整的实验溯源信息和分子特征标注，可直接支撑药物筛选、结构解析等研发环节，并作为AI大模型训练的核心数据底座。