MGACL:基于元图联想意识的对比学习预测药物蛋白相互作用
Pinglu Zhang1, Peng Lin2, Dehai Li2
1Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266003, China.
Biomolecules
|October 26, 2024
概括
这项研究引入了Meta Graph协会意识对比学习 (MGACL),以改善药物向相互作用的预测. MGACL减少了图形神经网络中的偏差和负转移,以实现更准确的药物发现.
科学领域:
- 计算化学和化学信息学
- 生物信息学和计算生物学
- 人工智能在药物发现中的作用
背景情况:
- 准确的药物向相互作用 (DTI) 识别对于有效的药物发现至关重要.
- 图形神经网络 (GNN) 对DTI预测有希望,但遭受偏差和负转移.
- 现有的方法很难有效地利用异构的辅助信息来进行个性化的DTI预测.
研究的目的:
- 开发一个适应性网络,减轻基于GNN的DTI预测中的偏差和负转移.
- 有效地传输个性化的异质辅助信息,以改善DTI识别.
- 为了提高药物向相互作用预测的准确性,用于新药开发.
主要方法:
- 介绍了Meta Graph协会意识的对比学习 (MGACL) 网络.
- 实施适应性个性化元知识转移机制,用于异构的辅助信息.
- 提出了一种新的DTI协会意识的对比学习策略,以调整药物表征并防止负面转移.
主要成果:
- 拟议的MGACL网络有效地传输个性化的异质辅助信息.
- 相反的学习策略成功地通过对齐表示来防止负面转移.
- 根据AUC和AUPRC指标,MGACL在DTI预测性能中取得了大约3%的改进,超过了现有的方法.
结论:
- MGACL提供了一个强大的框架来提高基于GNN的DTI预测准确度.
- 适应性元知识转移和对比学习方法有效地解决了偏见和负面转移.
- 这种进步有助于更精确地识别药物点,加速新药的开发.
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