Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Identification of non-toxic substructures: a new strategy to avoid potential toxicity risk

Identification of non-toxic substructures: a new strategy to avoid potential toxicity risk Abstract Avoidance of structural alerts (SA) might reduce the risk of failure in drug discovery. However, there are still some marketed drugs containing SA, which indicates that SA should be analyzed carefully to avoid their excessive uses. Several detection systems, including automatic mining methods and expert systems, have been developed to identify SA. These methods only focus on toxic compounds that support the SA without consideration of non-toxic ones. Here, we proposed a frequency-based substructure detection protocol that learns from the non-toxic compounds containing SA to get non-toxic substructures (NTS), whose appearance will reduce the probability of a compound becoming toxic. Kazius and Hansen’s Ames mutagenicity data set was used as an example to demonstrate the protocol. SARpy and ToxAlerts were first employed to obtain the potential SA. Then two kinds of NTS were exploited: reverse effect substructures (RES) and conjugate effect substructures (CES). Contribution and prediction performance of the substructures were evaluated via neural network and rule-based methods. We also compared substructure-based methods with the conventional machine learning-based methods. The results demonstrated that most substructures contributed as supposed and substructure-based methods performed better in the resistance of overfitting. This work indicated that the protocol could effectively reduce the false positive rate in prediction of chemical mutagenicity, and possibly extend to other endpoints. structural alerts, Ames mutagenicity, computational toxicology, reverse effect substructures, conjugate effect substructures © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Toxicological Sciences Oxford University Press

Identification of non-toxic substructures: a new strategy to avoid potential toxicity risk

Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun
Toxicological Sciences , Volume Advance Article – Jun 8, 2018
Download PDF
39 pages

Loading next page...
 
/lp/ou_press/identification-of-non-toxic-substructures-a-new-strategy-to-avoid-Nh0qXB10op
Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please email: journals.permissions@oup.com
ISSN
1096-6080
eISSN
1096-0929
DOI
10.1093/toxsci/kfy146
Publisher site
See Article on Publisher Site

Abstract

Abstract Avoidance of structural alerts (SA) might reduce the risk of failure in drug discovery. However, there are still some marketed drugs containing SA, which indicates that SA should be analyzed carefully to avoid their excessive uses. Several detection systems, including automatic mining methods and expert systems, have been developed to identify SA. These methods only focus on toxic compounds that support the SA without consideration of non-toxic ones. Here, we proposed a frequency-based substructure detection protocol that learns from the non-toxic compounds containing SA to get non-toxic substructures (NTS), whose appearance will reduce the probability of a compound becoming toxic. Kazius and Hansen’s Ames mutagenicity data set was used as an example to demonstrate the protocol. SARpy and ToxAlerts were first employed to obtain the potential SA. Then two kinds of NTS were exploited: reverse effect substructures (RES) and conjugate effect substructures (CES). Contribution and prediction performance of the substructures were evaluated via neural network and rule-based methods. We also compared substructure-based methods with the conventional machine learning-based methods. The results demonstrated that most substructures contributed as supposed and substructure-based methods performed better in the resistance of overfitting. This work indicated that the protocol could effectively reduce the false positive rate in prediction of chemical mutagenicity, and possibly extend to other endpoints. structural alerts, Ames mutagenicity, computational toxicology, reverse effect substructures, conjugate effect substructures © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

Toxicological Sciences – Oxford University Press

Published: Jun 8, 2018

References

  • Computational derivation of structural alerts from large toxicology data sets
    Ahlberg
  • Alarms about structural alerts
    Alves
  • Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents by the U.S. NCI/NTP
    Ashby