烯的化学空间:来自数据科学和人工智能的见解
Morteza Hosseini1, David M Pereira1
1REQUIMTE/LAQV, Laboratório de Farmacognosia, Departamento de Química, Faculdade de Farmácia, Universidade do Porto, R. Jorge Viterbo Ferreira, 4050-313 Porto, Portugal.
Pharmaceuticals (Basel, Switzerland)
|June 1, 2023
概括
这项研究使用数据科学系统地描述了59,833种烯,高精度地对子类进行了分类. 这种方法有助于预测植物化学和药理识别的新型烯分子子类.
科学领域:
- 自然产品化学 自然产品化学
- 化学信息学 化学信息学
- 计算生物学 计算生物学
背景情况:
- 烯是各种各样的天然产品,具有成熟的医疗应用.
- 了解烯多样性对于药物发现和植物化学至关重要.
- 需要一个系统的方法来分类众多已知的烯.
研究的目的:
- 系统地识别,编译和描述已知的烯多样性.
- 开发和评估基于物理化学性质分类子子类的计算方法.
- 为了能够预测新型分子的烯子类.
主要方法:
- 以数据科学为基础的方法应用于59,833个烯分子的数据集.
- 使用物理化学描述符进行分类.
- 评估了机器学习算法,包括梯度增强,k-最近邻居,随机森林,高斯素朴贝叶斯和多层感知器.
主要成果:
- 机器学习模型实现了高性能指标 (准确性,F1得分,精度>0.9) 的烯子类分类.
- 证明了计算方法在分类烯子类中的有效性.
- 成功地分类了众所周知的大量多样化的烯.
结论:
- 数据科学和机器学习是类分类的强大工具.
- 开发的方法可以预测新分子的烯子类,即使没有生物合成数据.
- 这些发现促进了植物化学和药理学,促进了烯的表征和发现.
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