通过弱依序的自行回归语言模型和终身学习实现统一的生成框架
Zhiwei Nie1,2, Daixi Li3, Yutian Liu4
1School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
Journal of chemical information and modeling
|August 1, 2025
概括
我们开发了PepGenWL,这是一种用于生成治疗性的新框架. 这种深度学习模型有效地捕捉了的相互作用和灵活性,在产生抗微生物和抗癌方面表现优于现有的方法.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 生物活性显示出治疗前景,推动了对先进生成模型的需求.
- 目前的深度生成模型在形灵活性和残留相互作用方面扎.
研究的目的:
- 引入PepGenWL,使用弱依赖顺序的自回归语言模型和终身学习来生成的统一框架.
- 解决现有模型关于形状灵活性和残留物依赖性的局限性.
主要方法:
- PepGenWL采用一种自回归语言模型,对异常输入有宽容性.
- 专家混合式插件在微调过程中平衡了内存稳定性和学习可塑性.
- 该框架在产生抗微生物,抗癌和结剂方面进行了评估.
主要成果:
- 在产生治疗性质方面,PepGenWL超越了最先进的模型,包括抗微生物和抗癌类型.
- 该模型展示了将有益的残留物纳入生物活性的能力.
- 一个属性导向管道实现了具有特异性的结剂的28.6%的结率目标.
- PepGenWL的适用性扩展到微笑,使得生成的修饰和循环.
结论:
- PepGenWL为通用生成提供了一个统一而灵活的框架.
- 该模型有效地处理的结构灵活性和残留物相互作用.
- PepGenWL显示了促进治疗性的发现和开发的巨大潜力.
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