MDNN-DTA:一种多式联络深度神经网络,用于药物向 afinity 预测
Xu Gao1,2, Mengfan Yan1,2, Chengwei Zhang1,2
1School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China.
Frontiers in genetics
|April 4, 2025
概括
我们开发了MDNN-DTA,这是一个深度学习模型,用于仅使用序列数据预测药物向亲和力 (DTA). 这种人工智能方法通过绕过复杂的3D蛋白质结构的需求来提高药物发现效率.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 药物发现 药物发现 药物发现
背景情况:
- 药物向亲和度 (DTA) 的确定在药物发现中至关重要.
- 在 silico 方法,特别是人工智能和深度学习,为实验性 DTA 预测提供了有效的替代方案.
- 从大规模的生物序列数据中预测DTA是一个重大挑战.
研究的目的:
- 介绍MDNN-DTA,一个新的多式联络深度神经网络用于DTA预测.
- 开发一种能够直接从药物和蛋白质序列中预测DTA的模型,消除了对3D蛋白质结构的需求.
- 使用先进的人工智能技术,增强从蛋白质序列中提取特征.
主要方法:
- 利用图形卷积网络 (GCN) 进行药物分子特征提取.
- 使用卷积神经网络 (CNN) 来提取蛋白质序列特征.
- 集成了一个ESM预训练模型和一个定制的蛋白质特征提取 (PFE) 块,用于全面的蛋白质序列分析,进一步增强了蛋白质特征融合 (PFF) 块.
主要成果:
- MDNN-DTA直接从序列数据中证明了有效的DTA预测.
- 该模型成功地从药物和蛋白质序列中提取了高维特征.
- 废弃研究证实了MDNN-DTA架构中的单个组件的性能和有效性.
结论:
- MDNN-DTA提供了一种强大而高效的AI驱动的方法,用于药物发现中的DTA预测.
- 该模型利用序列数据的能力克服了与蛋白质3D结构不可用性相关的局限性.
- MDNN-DTA代表了计算药物发现的重大进步,提高了效率并降低了成本.
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