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MCL-DTI:使用药物多式信息和双向交叉注意力学习方法来预测药物标相互作用
Ying Qian1, Xinyi Li1, Jian Wu1
1Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Computer Science and Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062, China.
BMC bioinformatics
|August 26, 2023
概括
本研究介绍了MCL-DTI,这是一种用于药物向相互作用 (DTI) 预测的深度学习模型. MCL-DTI有效地使用多式联络药物信息和注意力机制,以提高DTI预测的准确性.
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:
- 药物向相互作用 (DTI) 的预测对于药物发现和重新定位至关重要.
- 传统的DTI方法耗时;深度学习提供了一个有效的替代方案.
- 当前的深度学习方法通常使用单模药物信息,限制特征表示.
研究的目的:
- 开发一种新的深度学习模型,用于增强DTI预测.
- 利用多式药物信息 (分子图像和化学特征) 进行全面的药物表征.
- 通过双向多头注意力机制改进药物向相互作用学习.
主要方法:
- 拟议的MCL-DTI模型使用多式联络药物信息:分子图像和化学文本.
- 采用双向多头交叉注意力 (MCA) 机制来学习药物目标的相互关系.
- 实现了具有多头自我注意 (MSA) 的解码器和用于交叉信息学习的MCA块,其次是用于最终预测的融合块.
主要成果:
- 在三个数据集 (人类,C. elegans,戴维斯) 中,MCL-DTI实现了卓越的性能.
- 该模型在平衡和不平衡数据集上都表现出有效性.
- 结果表明DTI预测的功能学习强大.
结论:
- 在DTI预测任务中,MCL-DTI显示了最先进的性能.
- 该模型具有强大的概括能力,其在药物相互作用 (DDI) 任务上的表现证明了这一点.
- MCL-DTI可适应应用于其他相关的生物任务.
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