A QSAR study for predicting malformation in Zebrafish embryo

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 Quantitive 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 chemoinformatic software. The data were cleaned, preprocessed, and then split into training and testing sets. Machine learning algorithms, such as Gradient Boosting (GBM) and logistic regression (LR), as well as deep learning models, including multilayer perceptron (MLP) and neural networks (NN) 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 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. Conclusion The descriptors that were the most effective for developing models directly impact the mechanism of malformation, and gradient boosting is the best model due to its Matthews correlation coefficient (MCC) and balanced accuracy (BAC).