PyQSAR: A Fast QSAR Modeling Platform Using Machine Learning and Jupyter Notebook

Citations

WEB OF SCIENCE

18
Citations

SCOPUS

22

초록

Understanding the relationship between structure and property is important in current research works. The QSAR/QSPR (Quantitative Structure-Activity Relationship/Quantitative Structure-Property Relationship) is a common method for finding the relationships between the structure and property of compounds. However, traditional methods of performing QSAR analysis rely on multiple software platforms for each step. Here, an integrated standalone python package, PyQSAR, is proposed that combines all QSAR modeling process in one workbench. The efficiency of the package was verified by comparing to 10 previously published works. The results showed high performance of PyQSAR in terms of R-2 with less than half an hour execution time with a typical desktop PC for each test case. The main goal of PyQSAR is the production of reliable QSAR models on a single platform with an easy-to-follow workflow.

키워드

QSA(P)R; Chemoinformatics; Machine learning; Hierarchical clustering; Genetic algorithm; PREDICTION
제목
PyQSAR: A Fast QSAR Modeling Platform Using Machine Learning and Jupyter Notebook
저자
Kim, Sinyoung; Cho, Kwang-Hwi
DOI
10.1002/bkcs.11638
발행일
2019-01
유형
Article
저널명
Bulletin of the Korean Chemical Society
권
40
호
1
페이지
39 ~ 44