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

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
D.O.I.
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 SciencesOxford University Press

Published: Jun 8, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off