Enlarging Applicability Domain of Quantitative Structure–Activity Relationship Models through Uncertainty-Based Active Learning-Reference-Cited by-同舟云学术

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Enlarging Applicability Domain of Quantitative Structure–Activity Relationship Models through Uncertainty-Based Active Learning

Published:2022-01-31 Issue:7 Volume:2 Page:1211-1220
ISSN:2690-0645
Container-title:ACS ES&T Engineering
language:en
Short-container-title:ACS EST Eng.

Author:

Zhong Shifa¹^ORCID,Lambeth Dylan R¹,Igou Thomas K¹,Chen Yongsheng¹^ORCID

Affiliation:

1. School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States

Funder

U.S. Environmental Protection Agency

U.S. Department of Agriculture

National Science Foundation-U.S. Department of Agriculture

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsestengg.1c00434

Reference56 articles.

1. QSAR without borders

2. Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?

3. Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action

4. A Quantitative-Structure-Activity-Relationship (QSAR) model for the reaction rate constants of organic compounds during the ozonation process at different temperatures

5. Quantitative structure–activity relationships for reactivities of sulfate and hydroxyl radicals with aromatic contaminants through single–electron transfer pathway

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3. Evaluation and Optimization Methods for Applicability Domain Methods and Their Hyperparameters, Considering the Prediction Performance of Machine Learning Models;ACS Omega;2024-02-26

4. Cheminformatics and artificial intelligence for accelerating agrochemical discovery;Frontiers in Chemistry;2023-11-29

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