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M3S-GRPred:一种新的集体学习方法,用于使用多步叠加策略可解释地预测葡萄糖皮质体受体对抗剂

Nalini Schaduangrat¹, Hathaichanok Chuntakaruk^2,3,4, Thanyada Rungrotmongkol^2,3

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

BMC bioinformatics

|April 30, 2025

概括

我们开发了M3S-GRPred,这是一种新的机器学习工具,可以使用化学结构信息有效地识别葡萄糖皮质体受体 (GR) 反对者. 这种计算方法加速了对GR相关疾病的药物发现,为实验方法提供了具有成本效益的替代方案.

科学领域:

计算化学和化学信息学.
机器学习在药物发现中的应用.
药理学和治疗发展.

背景情况:

与葡萄糖皮质体受体 (GR) 相关的疾病需要有效的治疗方法,但传统的药物发现方法往往缓慢且昂贵.
识别GR抗剂对于治疗开发至关重要,但实验查不能扩展.
使用化学结构数据 (SMILES) 的计算方法为高效的in silico药物发现提供了一个有希望的途径.

研究的目的:

开发一种新,准确和具有成本效益的计算工具,用于识别葡萄糖皮质体受体 (GR) 抗剂.
创建第一个基于SMILES的GR对手预测器,不需要3D结构信息.
加速用于治疗应用的新型GR抗体的发现.

主要方法:

开发了一种多步叠加策略 (M3S) 组合学习方法,命名为M3S-GRPred.
利用不足样本来创建平衡的数据集,用于培训基准分类器.
采用各种基于SMILES的特征描述器和机器学习算法,然后进行特征选择和概率集成.

主要成果:

M3S-GRPred准确地识别GR对手,并有效地处理不平衡的数据集.
与传统的机器学习分类器相比,M3S-GRPred模型在培训和独立测试集上都表现出更高的性能.
应用M3S-GRPred来识别FDA批准的药物中潜在的GR抗剂,通过分子对接和分子动力学模拟来验证库辛综合征重定位.

结论:

M3S-GRPred是一种高度有效和高效的计算工具,用于发现新型GR对手.
这种方法为选大型化合物库提供了一种具有成本效益的方法,大大促进了对GR相关疾病的药物发现.
该工具显示了药物重定向的潜力,正如库辛综合征的研究表明的那样.

关键词:

化学信息学化学信息学库辛综合征是什么意思库辛综合征是什么意思功能选择功能选择葡萄糖皮质类受体受体机器学习机器学习多视图功能提供了多视图功能.在QSAR中使用QSAR.

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