Development of Machine Learning-Based Models for Mutagenicity Predictions with Applications to Non-Sugar Sweeteners.

Abstract

Artificial sweeteners, often known as non-sugar sweeteners (NSSs), have been utilized as food additives since World War II. However, there is also concern regarding the mutagenicity potential of NSSs. Every new chemical registration in the food and pharmaceutical industries requires an evaluation of its mutagenic potential, which is essential for food safety. Most of the studies focus solely on determining the mutagenicity of NSSs through in vivo trials, which may be troublesome in terms of the time and cost required for experimental evaluation. To avoid the associated complexities concerning experimentation, a new approach methodology by developing machine learning (ML) models for mutagenicity predictions and selecting the best models by a stringent cross-validation analysis is explored. Two random splits (50/50) of a dataset of 6881 organic compounds for model development are used. Consensus predictions are provided for the mutagenic potential of an external set of 332 NSSs using six selected models (three best ML models based on cross-validation using either data splitting strategy) through voting and considering the applicability domain using two different approaches. In addition, to check the reliability of predictions, the model-derived consensus predictions have also been compared to the predictions generated by the k-nearest neighbor method using the virtual models for property evaluation of chemicals within a global architecture platform and the consensus method generated in the toxicity estimation software tool platform. Finally, based on the analysis, six compounds could be prioritized as mutagenic NSSs in this investigation. The developed models have been made available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home/mutagenicity-predictor.