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MetaCGRP是一种高精度的元模型,用于使用多视图信息大规模识别CGRP抑制剂

Nalini Schaduangrat¹, Phisit Khemawoot², Apisada Jiso²

¹Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.

Scientific reports

|October 21, 2024

概括

这项研究介绍了MetaCGRP,这是一种用于识别素基因相关 (CGRP) 抑制剂的新型机器学习模型. 这种方法可以加速发现新的偏头痛药物,而不需要3D结构数据.

科学领域:

计算化学和生物信息学
药物的发现和开发.
神经科学和药理学是神经科学和药理学.

背景情况:

偏头痛是全球残疾的主要原因,影响全球14-15%的人口.
素基因相关 (CGRP) 是偏头痛治疗的关键目标,但药物开发是昂贵和耗时的.
机器学习 (ML) 为加速识别CGRP抑制剂提供了一个有希望的途径.

研究的目的:

开发一种新的,高精度的元模型,MetaCGRP,用于精确识别CGRP抑制剂.
建立第一个基于SMILES的CGRP抑制剂识别方法,消除对3D结构信息的需求.
加速发现用于偏头痛治疗的新型CGRP向药物.

主要方法:

使用各种分子表示和算法构建基线ML模型.
优化基线模型以生成多视图功能.
应用特征选择来识别超模构建的最佳特征.

主要成果:

MetaCGRP实现了高精度,在培训数据集上达到0.898,在独立测试数据集上达到0.799.
该模型在识别CGRP抑制剂方面超过了几种传统的ML分类器.
结合分子对接的MetaCGRP,从泰国草本药典中确定了五种潜在的天然产品候选者.

结论:

在CGRP抑制剂的计算药物发现方面,MetaCGRP代表了重大进展.
基于SMILES的方法为识别新型偏头痛治疗方法提供了更快,更具成本效益的方法.
一个用户友好的网络服务器可用于支持社区范围内的CGRP抑制剂发现工作.

关键词:

卡尔西托宁基因相关的.化学信息学化学信息学功能选择功能选择机器学习是机器学习.超级模型是什么?在QSAR中使用QSAR.

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