MOFormer:通过基于帕雷托的多目标变压器在抗微生物设计空间进行导航
Li Wang1, Xiangzheng Fu2, Jiahao Yang3
1Department of Computer Science, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan.
Briefings in bioinformatics
|November 8, 2025
概括
深度学习管道MOFormer设计了具有多种最佳性质的抗微生物 (AMP). 这种先进的方法通过平衡活性,血液溶解和毒性来加速发现有效的抗生素.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 深度学习推进了用于新型抗生素开发的AMP设计.
- 同时优化多个AMP属性 (活动,安全) 是一个关键的挑战.
研究的目的:
- 介绍MOFormer,一个用于同时优化AMP属性的多目标管道.
- 增强具有所需特性的抗生素的生成.
主要方法:
- 使用条件变压器架构进行AMP序列属性景观改进.
- 为结构化设计空间和精确的候选样本采集集成的规范化技术.
- 雇员帕雷托前线分析,以对候选人的层次排名进行分析.
主要成果:
- MOFormer实现了卓越的多目标优化,优于现有方法.
- 证明有效同时最大化抗微生物活性,并最大限度地减少血液溶解和毒性.
- 使用AlphaFold预测 (70-87%pLDDT) 验证生成的候选物质的特性和结构完整性.
结论:
- MOFormer通过高效优化多目标权衡来加速抗生素的发现.
- 管道显示产生有效和安全的抗微生物的承诺.
- 这种方法可以显著影响未来的抗生素开发策略.
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