以注意力为基础的方法来预测七个目标超级家族的药物向相互作用
Aron Schulman1, Juho Rousu2, Tero Aittokallio1,3,4,5
1Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, 00014, Finland.
Bioinformatics (Oxford, England)
|August 8, 2024
概括
这项研究引入了MMAtt-DTA,这是一种基于注意力的方法,用于预测药物向相互作用. 这种新的方法准确地识别了潜在的药物点,有助于药物的重新用途和癌症等复杂疾病的机制发现.
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.
- 生物信息学是一种生物信息学.
背景情况:
- 药物向相互作用 (DTI) 对于理解药物机制和实现药物重定向至关重要.
- 像癌症这样的复杂疾病涉及多种生物过程,需要对药物相互作用的全面了解,而不仅仅是单一的目标.
- 人类蛋白质组有众多可用药的点,但大多数药物只与一个小的子集相互作用.
研究的目的:
- 开发和验证一种基于注意力的方法 (MMAtt-DTA),用于预测人类蛋白超级家族的药物向生物活性.
- 为新型DTI预测确定最佳分子描述符.
- 为药物机制发现和重新利用提供一个工具.
主要方法:
- 开发了一个基于注意力的深度学习模型 (MMAtt-DTA).
- 评估了九个不同的描述器集,以找到DTI预测的最佳签名.
- 该模型在人类蛋白质超级家族上进行了训练和测试,并使用独立的生物活性数据和ChEMBL数据库条目进行了验证.
主要成果:
- MMAtt-DTA实现了高预测准确度,在七个目标超级家族中,六个超级家族的斯皮尔曼相关性超过0.72 (P < 0.001).
- 该方法超过了14种最先进的DTI预测方法.
- 在超过185,000种药物标对的计算验证显示出强大的性能 (斯皮尔曼相关性>0.57,P<0.001).
结论:
- 开发的基于注意力的方法 (MMAtt-DTA) 在预测新型药物向相互作用方面表现出强度和高性能.
- 该研究成功地确定了DTI预测的最佳描述符.
- MMAtt-DTA是推动药物发现,机制阐明和现有药物的重新用途的宝贵工具.
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