Comparative Study of Machine-Learning and Chemometric Tools for Analysis of In-Vivo High-Throughput Screening Data-Reference-Cited by-同舟云学术

Comparative Study of Machine-Learning and Chemometric Tools for Analysis of In-Vivo High-Throughput Screening Data

Published:2008-08-01 Issue:8 Volume:48 Page:1663-1668
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Simmons Kirk¹,Kinney John¹,Owens Aaron¹,Kleier Dan¹,Bloch Karen¹,Argentar Dave¹,Walsh Alicia¹,Vaidyanathan Ganesh¹

Affiliation:

1. Simmons Consulting, 52 Windybush Way, Titusville, New Jersey 08560, DuPont Stine Haskell Research Laboratories, 1090 Elkton Road, Newark, Delaware 19711 DuPont Engineering Research and Technology, POB 80249, Wilmington, Delaware 19880-0249, Drexel University, 3141 Chestnut Street, Philadelphia, Pennsylvania 19104, Sun Edge, LLC, 147 Tuckahoe Lane, Bear, Delaware 19701, and, Quantum Leap Innovations, 3 Innovation Way, Suite 100, Newark, Delaware 19711

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/ci800142d

Reference30 articles.

1. Prediction of the Brain−Blood Distribution of a Large Set of Drugs from Structurally Derived Descriptors Using Partial Least-Squares (PLS) Modeling

2. Use of Statistical and Neural Net Approaches in Predicting Toxicity of Chemicals

3. Prediction of Henry's Law Constants by a Quantitative Structure Property Relationship and Neural Networks

4. QSAR/QSPR Studies Using Probabilistic Neural Networks and Generalized Regression Neural Networks

5. Support Vector Machines for the Estimation of Aqueous Solubility

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