Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks
Author:
Affiliation:
1. Department of Mathematics, ‡Department of Electrical and Computer Engineering, and ¶Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
Funder
Division of Mathematical Sciences
Division of Information and Intelligent Systems
Center for Mathematical Molecular Biosciences, Michigan State University
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.7b00558
Reference50 articles.
1. In Silico Quantitative Structure Toxicity Relationship of Chemical Compounds: Some Case Studies
2. QSAR and k-Nearest Neighbor Classification Analysis of Selective Cyclooxygenase-2 Inhibitors Using Topologically-Based Numerical Descriptors
3. Three-Dimensional QSAR Using the k-Nearest Neighbor Method and Its Interpretation
4. Quantitative structure activity relationship model for predicting the depletion percentage of skin allergic chemical substances of glutathione
5. Prediction of Fungicidal Activities of Rice Blast Disease Based on Least-Squares Support Vector Machines and Project Pursuit Regression
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