使用3D分子指纹的文字挖掘来预测抗神经炎症剂的NO分类器预测
Si Eun Lee1, Sangjin Ahn1,2, Surendra Kumar1
1Gachon Institute of Pharmaceutical Science, Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea.
Scientific reports
|November 16, 2024
概括
这项研究引入了一种新的表型结构关系模型,用于预测抗神经炎症药物功效. NO-分类器根据分子结构准确识别氧化抑制剂,帮助中枢神经系统发现药物.
科学领域:
- 神经科学和药理学 神经科学和药理学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 中枢神经系统 (CNS) 药物发现面临挑战,因为人们对疾病机制的理解不足.
- 现型效应指导了一些中枢神经系统药物的开发,尽管目标含糊不清.
- 基于分子结构预测药物的疗效为中枢神经系统治疗提供了一个有希望的途径.
研究的目的:
- 开发基于3D分子结构的抗神经炎症功能的预测模型.
- 在没有目标规范的情况下,为中枢神经系统药物发现建立表型结构关系模型.
- 构建和验证微质中氧化 (NO) 抑制功能的分类器.
主要方法:
- 一种以化疗为中心的方法,使用来自95篇研究文章的548种药物,并通过9篇其他文章的外部验证.
- 3D分子结构被编码到E3FP分子指纹中,用于3D表示.
- 包括MLP,RNN和CNN在内的机器学习模型被用于对NO抑制功能的二进制分类 (IC50截止值:37.1μM).
主要成果:
- 开发的NO-分类器模型 (MLP,RNN,CNN) 显示出高性能,具有出色的交叉验证和测试集AUC值 (例如,RNN AUC测试:0.995).
- 没有分类器模型的性能超过了经典的机器学习模型,如Logistic,Ridge,Lasso和Naïve Bayes.
- 独立的验证证实了NO-分类器在识别炎症细胞表型中的抗炎功效方面的有效性.
结论:
- 现型结构关系模型有效地预测了使用3D分子结构的抗神经炎症功效.
- NO-分类器为识别潜在的中枢神经系统抗炎药物提供了有价值的工具.
- 有一个Web服务器 (https://no-classifier.onrender.com) 可用于使用NO-Classifier工具.
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