通过保持注意力分布的一致性来预测药物向相互作用的多维搜索
Huaihu Li1, Shunfang Wang2, Weihua Zheng1
1Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China.
Computational biology and chemistry
|October 16, 2023
概括
MdDTI是一种新型的神经网络,通过分析二维子结构和三维空间药物结构来预测药物向相互作用. 这种方法通过比现有方法更有效地识别潜在的结合部位来增强药物重新用途和开发.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 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) 的预测对于药物重新定位和开发至关重要.
- 目前的方法通常依赖于简化分子输入线输入系统 (SMILES) 和2D分子图,限制了全面的分析.
- 需要先进的模型,整合多维药物结构信息,以准确预测DTI.
研究的目的:
- 引入MdDTI,一种用于预测药物向相互作用的新型神经网络模型.
- 探索多维药物结构 (2D子结构和3D空间) 对DTI预测的实用性.
- 通过增强的DTI预测,提高药物重定向和新药开发的准确性和有效性.
主要方法:
- 开发了一种用于二维药物分子图的新型基结构分解策略,并将其与基于SMILES的方法进行比较.
- 使用药物中的重原子的笛卡尔坐标构建了3D空间特征表示矩阵.
- 在神经网络框架内集成2D和3D结构信息,并引入了一致性损失函数.
主要成果:
- 与最先进的模型相比,MdDTI在四个DTI数据集和三个独立的化合物-蛋白质亲和力测试集中表现出优异的性能.
- 该模型有效地确定了药物和目标之间潜在的绑定区域.
- 案例研究证实了该模型在药物重定向应用中的有效性.
结论:
- 通过利用多维药物结构信息,MdDTI在DTI预测方面取得了重大进展.
- 该模型捕获结合点的能力提高了其在药物发现和重新利用管道中的实用性.
- 拟议的方法为未来的DTI预测研究提供了一个强大的框架.
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