MMSSC-Net:用于药物分子识别的多级序列认知网络
Dehai Zhang1, Di Zhao1, Zhengwu Wang1
1The Key Laboratory of Software Engineering of Yunnan Province, School of Software, Yunnan University Kunming China lijin@ynu.edu.cn.
RSC advances
|June 10, 2024
概括
本研究介绍了MMSSC-Net,这是一个新的多阶段神经网络,用于准确地解释矢量图形中的药物结构. 这种方法提高了计算机对分子数据的识别能力,有助于药物发现.
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
- 人工智能在药物发现中的作用
背景情况:
- 药物化合物结构通常以2D矢量图形表示,这给计算识别和利用带来了挑战.
- 当前的光学化学结构识别 (OCSR) 方法通常将结构处理为孤立的实体,限制细粒度分析.
研究的目的:
- 开发一个多阶段的认知神经网络模型,用于细粒度预测和解释分子向量图形.
- 提高药物结构表示的计算机可读性和分析能力.
主要方法:
- 采用了自下而上,分阶段的认知方法,从原子和键表征作为离散标签序列开始.
- 随后的阶段从标签序列构建分子图形,并将其演变为机器可读的格式.
- 该模型,MMSSC-Net,使用序列认知方法来提高可解释性和可转移性.
主要成果:
- 与现有的先进方法相比,MMSSC-Net在多个公共数据集上表现出卓越的性能.
- 在各种分辨率的认知识别中实现了高准确率 (75-94%).
- 该模型在解释性和可转移性方面被证明是可靠的.
结论:
- MMSSC-Net提供了一种更有效的方法,用于计算机辅助识别分子向量图形.
- 这种方法为药物信息的发现和化学空间的探索提供了新的途径.
- 阶段性认知方法提高了分子结构分析的可靠性和适用性.
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