A QSAR study for predicting malformation in zebrafish embryo

Abstract

Background

Developmental toxicity tests are extremely expensive, require a large number of animals, and are time-consuming. It is necessary to develop a new approach to simplify the analysis of developmental endpoints. One of these endpoints is malformation, and one group of ongoing methods for simplifying is in silico models. In this study, we aim to develop a quantitative structure–activity relationship (QSAR) model and identify the best algorithm for predicting malformations, as well as the most important and effective physicochemical properties associated with malformation.

Methods

The dataset was extracted from a reliable database called COMPTOX. Physicochemical properties (descriptors) were calculated using Mordred and RDKit chemoinformatics software. The data were cleaned, preprocessed, and then split into training and testing sets. Machine learning algorithms, such as gradient boosting model (GBM) and logistic regression (LR), as well as deep learning models, including multilayer perceptron (MLP) and neural networks (NNs) trained with train set data and different sets of descriptors. The models were then validated with test set and various statistical parameters, such as Matthew’s correlation coefficient (MCC) and balanced accuracy (BAC) score, were used to compare the models.

Results

A set of descriptors containing with 78% AUC was identified as the best set of descriptors. Gradient boosting was determined to be the best algorithm with 78% predictive power.

Conclusions

The descriptors that were the most effective for developing models directly impact the mechanism of malformation, and GBM is the best model due to its MCC and BAC.

Keywords:

• Malformation
• developmental toxicity
• gradient boosting
• predictive toxicology
• in silico test

Author contributions

Mahsa Daneshmand: as PhD candidate she was responsible for designing the idea of the study, analysis and gathering the data and preparing the primitive version of the manuscript. Jamileh SalarAmoli: the study design and supervision were mainly conducted by her. She also played a substantial role in the writing and editing of the manuscript. All the authors have read and approved the submission of the manuscript. Negin Baghbanzadeh: she had contribution in study design and methodology of the study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors assert that all data supporting the findings of this study are available in supplementary data. For more information, you can email [email protected]

Additional information

Funding

This project was supported by Research Center of Toxicology, located in Faculty of Veterinary Medicine, University of Tehran, Iran.