The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity
Author:
Affiliation:
1. Cheminformatics Department, RY800B-305, Merck Research Laboratories, Rahway, New Jersey 07065, United States
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.5b00110
Reference42 articles.
1. QM/NN QSPR Models with Error Estimation: Vapor Pressure and LogP
2. Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR
3. A Stepwise Approach for Defining the Applicability Domain of SAR and QSAR Models
4. Determining the Validity of a QSAR Model − A Classification Approach
5. Can we estimate the accuracy of ADME–Tox predictions?
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