AbEgDiffuser:抗体序列结构编码设计与等价图神经网络和扩散模型
Yibo Zhu1, Xiumin Shi1, Jingjuan Zhang1
1School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
Journal of chemical theory and computation
|October 30, 2025
概括
AbEgDiffuser是一个深度生成框架,为药物发现设计了抗体序列和结构. 这种人工智能模型产生具有高结合亲和力的精确抗体,优于传统方法.
科学领域:
- 生物技术是生物技术.
- 免疫学 免疫学 免疫学
- 人工智能在药物发现中的作用
背景情况:
- 抗体是具有高特异性的重要免疫蛋白.
- 传统的抗体工程是低效和耗时的.
- 深度学习为抗体设计和药物发现提供了一种创新的方法.
研究的目的:
- 引入AbEgDiffuser,一个用于抗体序列和结构代码设计的深度生成框架.
- 为了使对特定向抗原有条件的抗体设计.
- 提高治疗应用抗体开发的效率和准确性.
主要方法:
- 扩散模型与等价图神经网络的集成.
- 使用ESM-2蛋白语言模型将进化约束纳入.
- 逐渐腐蚀和重建抗体序列,Cα原子坐标和残留方向.
主要成果:
- AbEgDiffuser成功地产生了具有精确序列和结构的抗体.
- 设计的抗体对目标抗原具有很高的结合亲和力.
- 该框架的性能优于现有的新型抗体设计和优化方法.
结论:
- AbEgDiffuser为抗体设计提供了一个强大的AI驱动的解决方案.
- 该模型通过提高抗体工程效率来增强新疗法的开发.
- 这项工作推进了用于药物发现的计算抗体设计领域.
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