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Structure-Based Predictions of Activity Cliffs

Published:2015-05-11 Issue:5 Volume:55 Page:1062-1076
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Husby Jarmila¹,Bottegoni Giovanni¹,Kufareva Irina²,Abagyan Ruben²,Cavalli Andrea¹³

Affiliation:

1. Department of Drug Discovery and Development−Computation, IIT−Istituto Italiano di Tecnologia, 16163 Genova, Italy

2. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California—San Diego, La Jolla, California 92161, United States

3. Department of Pharmacy and Biotechnology, Università di Bologna, 40126 Bologna, Italy

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/ci500742b

Reference70 articles.

1. On Outliers and Activity CliffsWhy QSAR Often Disappoints

2. Exploring Activity Cliffs in Medicinal Chemistry

3. Recent Progress in Understanding Activity Cliffs and Their Utility in Medicinal Chemistry

4. Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde?

5. On the validity versus utility of activity landscapes: are all activity cliffs statistically significant?

Cited by 31 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. Anatomy of Potency Predictions Focusing on Structural Analogues with Increasing Potency Differences Including Activity Cliffs;Journal of Chemical Information and Modeling;2023-11-09

2. Exploring QSAR models for activity-cliff prediction;Journal of Cheminformatics;2023-04-17

3. Development and evaluation of a java-based deep neural network method for drug response predictions;Frontiers in Artificial Intelligence;2023-03-23

4. Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity;Journal of Cheminformatics;2023-01-07

5. Exposing the Limitations of Molecular Machine Learning with Activity Cliffs;Journal of Chemical Information and Modeling;2022-12-01

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