Corrigendum: In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
Hongbin Yang
Lixia Sun
Weihua Li
Guixia Liu
Yun Tang- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
A corrigendum on
In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
by Yang, H., Sun, L., Li, W., Liu, G., and Tang, Y. (2018). Front. Chem. 6:30. doi: 10.3389/fchem.2018.00030
In the original article, there was an error.
The Equation (6) was:
A correction has been made to Model Building With Machine Learning Methods, Model Evaluation, Equation (6):
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way.
The original article has been updated.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Keywords: drug safety, chemical toxicity, drug design, machine learning, structural alerts
Citation: Yang H, Sun L, Li W, Liu G and Tang Y (2018) Corrigendum: In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts. Front. Chem. 6:129. doi: 10.3389/fchem.2018.00129
Received: 25 March 2018; Accepted: 04 April 2018;
Published: 13 April 2018.
Edited and reviewed by: Daniela Schuster, Paracelsus Medizinische Privatuniversität, Austria
Copyright © 2018 Yang, Sun, Li, Liu and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yun Tang, ytang234@ecust.edu.cn