3D图形神经网络带有少数射击学习,用于预测基于脚手架的冷启动场景中的药物相互作用
Qiujie Lv1, Jun Zhou1, Ziduo Yang1
1School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
概括
预测新药的药物相互作用 (DDI) 是非常重要的. "Meta3D-DDI"是一个3D图形神经网络,具有少量学习,在冷启动场景中有效预测DDI,即使数据有限.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能是人工智能.
背景情况:
- 药物相互作用 (DDI) 存在风险,需要对新药进行准确的预测.
- "冷启动"情景,对于新药的数据有限,提出了一个重大挑战.
- 三维 (3D) 分子构造对于理解药物特性至关重要.
研究的目的:
- 开发一个新的3D图形神经网络 (3DGNN) 模型,Meta3D-DDI,用于预测冷启动场景中的DDI.
- 为了解决由3D分子异质性引起的少数射击学习中的空间混乱.
- 建立一个基于脚手架的强大的冷启动设置,以防止数据泄露.
主要方法:
- 提出的Meta3D-DDI,一个3DGNN,通过对对原子距离结合旋转/转换不变性.
- 实现了用于原子相互作用模拟的连续波器相互作用模块.
- 开发了一种使用双层优化进行知识转移的几次学习 (FSL) 策略.
- 引入了基于脚手架的冷启动场景,以确保训练和测试集之间有明确的药物脚手架.
主要成果:
- 在基于脚手架的冷启动场景下,Meta3D-DDI在DDI预测中取得了最先进的 (SOTA) 性能.
- 通过视觉实验,在新药DDI预测的学习中表现出显著的改善.
- 展示了该模型能够减少用于有意义的DDI预测所需的数据的能力.
结论:
- 在具有挑战性的冷启动场景中,Meta3D-DDI为DDI预测提供了强大的解决方案.
- 该模型的3D感知架构和FSL策略提高了准确性和数据效率.
- 基于脚手架的冷启动设置提供了对新药DDI预测模型的更现实的评估.
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