一个改进的基于融合网络的多模式表示-学习模型,用于药物发现中的财产预测
Jinzhou Wu1, Yang Su1, Ao Yang2
1School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China.
Computers in biology and medicine
|September 10, 2023
概括
这项研究引入了一种新的深度学习 (DL) 框架,即多模分子表示学习融合网络 (MMRLFN),用于药物发现. MMRLFN集成了多个分子表示,以提高分子性质预测的准确性.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 药物发现 药物发现
背景情况:
- 准确的分子表示对于药物发现中的深度学习 (DL) 至关重要.
- 以前的DL模型经常使用单分子表示,限制了特征捕获.
- 整合不同的分子特征可以改善属性预测.
研究的目的:
- 开发一个新的DL框架,多模态分子表示学习融合网络 (MMRLFN).
- 从图形和SMILES序列中实现分子特征的同时学习和集成.
- 为了提高药物发现分子性质预测的准确性和全面性.
主要方法:
- 开发了MMRLFN,一个多模式DL框架.
- 来自分子图和SMILES序列的综合特征,使用三个互补的神经网络.
- 在八个公共药物发现数据集上训练和评估模型.
主要成果:
- 与现有的单模DL模型相比,MMRLFN表现出更高的性能.
- 该框架有效地捕捉了分子拓,局部化学上下文和子结构.
- 分析证实了MMRLFN的抗噪能力,可解释性和概括能力.
结论:
- 通过整合不同的表示方式,MMRLFN可以准确地预测分子性质.
- 该框架提供了来自大型数据集的宝贵见解,有助于药物发现.
- MMRLFN提高了识别潜在候选药物的效率和成功率.
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