Building Highly Reliable Quantitative Structure–Activity Relationship Classification Models Using the Rivality Index Neighborhood Algorithm with Feature Selection-Reference-Cited by-同舟云学术

Building Highly Reliable Quantitative Structure–Activity Relationship Classification Models Using the Rivality Index Neighborhood Algorithm with Feature Selection

Published:2020-01-15 Issue:1 Volume:60 Page:133-151
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Ruiz Irene Luque¹^ORCID,Gómez-Nieto Miguel Ángel¹^ORCID

Affiliation:

1. Department of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, E-14071 Córdoba, Spain.

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.9b00706

Reference37 articles.

1. Current Mathematical Methods Used in QSAR/QSPR Studies

2. Contemporary QSAR Classifiers Compared

3. The Recent Trend in QSAR Modeling - Variable Selection and 3D-QSAR Methods

4. QSAR Modeling: Where Have You Been? Where Are You Going To?

5. Development and application of novel molecular descriptors for predicting biological activity

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An efficient drug compound analysis using spectral deep feature classification based compound analyse model for drug recommendation;Neuroscience Informatics;2022-06

2. Explainable Molecular Sets: Using Information Theory to Generate Meaningful Descriptions of Groups of Molecules;Journal of Chemical Information and Modeling;2021-10-12

3. WITHDRAWN: An efficient drug compound analysis model based on mutual spectral scaling feature selection using spectral deep feature classification for drug recommendation from health care;Neuroscience Informatics;2021-09

4. A value-based approach for training of classifiers with high-throughput small molecule screening data;Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics;2021-08

5. Antiplasmodial activity of sulfonylhydrazones: in vitro and in silico approaches;Future Medicinal Chemistry;2021-02