期刊
MOLECULAR PHARMACEUTICS
卷 7, 期 5, 页码 1708-1714出版社
AMER CHEMICAL SOC
DOI: 10.1021/mp100103e
关键词
Phospholipidosis; machine learning; Random Forest; Support Vector Machine; in silico; prediction
资金
- EPSRC [EP/F049102/1]
- Scottish Universities Life Sciences Alliance (SULSA)
- EPSRC [EP/F049102/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/F049102/1] Funding Source: researchfish
Phospholipidosis is an adverse effect caused by numerous cationic amphiphilic drugs and can affect many cell types. It is characterized by the excess accumulation of phospholipids and is most reliably identified by electron microscopy of cells revealing the presence of lamellar inclusion bodies. The development of phospholipidosis can cause a delay in the drug development process, and the importance of computational approaches to the problem has been well documented. Previous work on predictive methods for phospholipidosis showed that state of the art machine learning methods produced the best results. Here we extend this work by looking at a larger data set mined from the literature. We find that circular fingerprints lead to better models than either E-Dragon descriptors or a combination of the two. We also observe very similar performance in general between Random Forest and Support Vector Machine models.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据