OPTUNA optimization for predicting chemical respiratory toxicity using ML models

Predicting molecular toxicity is an important stage in the process of drug discovery. It is directly related to medical destiny and human health. This paper presents an enhanced model for chemical respiratory toxicity prediction. It used a combination of molecular descriptors and term frequency - inverse document frequency (TF-IDF) based models with different machine learning algorithms. To address class imbalance, SMOTE is applied. Appropriate hyper-parameter tuning is required to generate a better system with a classifier. So, we adjusted the hyper-parameters of various models and used the adjusted parameters to train the model. We tuned hyper-parameters using OPTUNA. Internal and external validation were used to confirm the models' performance. According to the results, the model's internal validation accuracy and AUC using the random forest approach were 88.6% and 93.2%. For external validation, the model's accuracy value using random forest and Gradient Boosting Classifier were 92.2% with AUC 97%. Comparing these results with previous studies shows that our model performs better compared to them.