PPII-AEAT:基于自编码器的蛋白质-蛋白质相互作用抑制剂的预测与对抗性训练
Zitong Zhang1, Lingling Zhao1, Mengyao Gao1
1Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, China.
Computers in biology and medicine
|March 19, 2024
概括
一种新的计算方法,PPII-AEAT,通过自适应学习分子表征,准确地预测蛋白质-蛋白质相互作用 (PPI) 抑制剂. 这种方法克服了现有的机器学习模型在药物发现方面的局限性.
科学领域:
- 计算化学是一种计算化学.
- 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:
- 蛋白与蛋白相互作用 (PPI) 越来越多地被认为是新疗法的关键目标.
- 开发用于PPI的小分子抑制剂对于疾病治疗和预防至关重要.
- 目前用于PPI抑制剂预测的机器学习模型通常需要手动功能工程,并且具有有限的概括性.
研究的目的:
- 引入一种新的计算方法,PPII-AEAT (蛋白质-蛋白质相互作用抑制剂-自编码器对抗训练),用于准确和可概括的PPI抑制剂预测.
- 开发一种能够适应性学习分子表示形式的模型,用于各种PPI目标.
主要方法:
- 利用扩展连接指纹和Mordred描述符从小分子中提取初始特征.
- 采用了三阶段训练的自动编码架构来学习高级分子表示.
- 综合对抗性培训,以增强模型的适应性学习能力,以满足不同PPI目标.
主要成果:
- 对九个不同的PPI目标和两个预测任务 (抑制剂识别和抑制功效) 评估了PPII-AEAT方法.
- 证明PPII-AEAT在预测PPI抑制剂方面明显优于现有的最先进方法.
- 展示了该模型在各种PPI目标的自适应学习表征中的有效性.
结论:
- 拟议的PPII-AEAT方法为预测蛋白质-蛋白质相互作用的小分子抑制剂提供了一种强大而可泛化的方法.
- 这一进步解决了在药物开发中大规模PPI抑制剂查中需要有效的计算工具的需求.
- 在计算药物发现领域,PPII-AEAT比传统的机器学习技术有了显著的改进.
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