In silico prediction of chemical reproductive toxicity using machine learning

Abstract

Reproductive toxicity is an important regulatory endpoint in health hazard assessment. Because the in vivo tests are expensive, time consuming and require a large number of animals, which must be killed, in silico approaches as the alternative strategies have been developed to assess the potential reproductive toxicity (reproductive toxicity) of chemicals. Some prediction models for reproductive toxicity have been developed, but most of them were built only based on one single endpoint such as embryo teratogenicity; therefore, these models may not provide reliable predictions for toxic chemicals with other endpoints, such as sperm reduction or gonadal dysgenesis. Here, a total of 1823 chemicals for reproductive toxicity characterized by multiple endpoints were used to develop structure‐activity relationship models by six machine‐learning approaches with nine molecular fingerprints. Among the models, MACCSFP‐SVM model has the best performance for the external validation set (area under the curve = 0.900, classification accuracy = 0.836). The applicability domain was analyzed, and a rational boundary was found to distinguish inaccurate predictions and accurate predictions. Moreover, several structural alerts for characterizing reproductive toxicity were identified using the information gain combining substructure frequency analysis. Our results would be helpful for the prediction of the reproductive toxicity of chemicals.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

“…Apart from the above major toxicities, many other toxicities have been studied for developing prediction models [72,[176][177][178][179][180][181][182][183]. The Lei et al research group recently published respiratory toxicity and urinary tract toxicity prediction studies [176,177].…”

Structure-Activity Relationship (SAR) Model for Predicting Teratogenic Risk of Antiseizure Medications in Pregnancy by Using Support Vector Machine

“…In preclinical studies, zebrafish embryo model and the animal models have also been established to evaluate the developmental toxicity, and teratogenic activity of the drugs (Murayama et al, 2018;Chetot et al, 2020;Jarque et al, 2020;Nguyen et al, 2021;Simeon et al, 2021). In addition, a few in silico models have been proposed to predict the reproductive toxicity of the chemical compounds (Basant et al, 2016;Jiang et al, 2019), but none of them is developed for the purpose of teratogenic risk prediction.…”

In silico prediction models for thyroid peroxidase inhibitors and their application to synthetic flavors

“…Currently, many studies and programs on machinelearning-based quantitative structure-activity relationship (QSAR) and in silico toxicity prediction were conducted Idakwo et al, 2018;Jiang et al, 2019;Li et al, 2017;Zhang et al, 2018;Zhang et al, 2020). Results from high-throughput experiments may be employed to build a model.…”

“…In addition, learning methods also play a significant role in model performance. Simple but powerful learning methods, such as support vector machines (SVMs), random forests (RFs), and artificial neural networks (ANNs), demonstrated good performance and were used in many previous QSAR studies Jiang et al, 2019;Li et al, 2014;Li et al, 2017). Recently, many ensemble-learning approaches were also introduced to improve the performance of models (Ai et al, 2019;Sheffield and Judson, 2019;Zhang et al, 2018).…”