Toxicity Prediction for Immune Thrombocytopenia Caused by Drugs Based on Logistic Regression with Feature Importance

Background: One of the problems in drug discovery that can be solved by artificial intelligence is toxicity prediction. In drug-induced immune thrombocytopenia, toxicity can arise in patients after five to ten days by significant bleeding caused by drugdependent antibodies. In clinical trials, when this condition occurs, all the drugs consumed by patients should be stopped, although sometimes this is not possible, especially for older patients who are dependent on their medication. Therefore, being able to predict toxicity in drug-induced immune thrombocytopenia is very important. Computational technologies, such as machine learning, can help predict toxicity better than empirical techniques owing to the lower cost and faster processing. Objective: Previous studies used the KNN method. However, the performance of these approaches needs to be enhanced. This study proposes a Logistic Regression to improve accuracy scores. Methods: In this study, we present a new model for drug-induced immune thrombocytopenia using a machine learning method. Our model extracts several features from the Simplified Molecular Input Line Entry System (SMILES). These features were fused and cleaned, and the important features were selected using the SelectKBest method. The model uses a Logistic Regression that is optimized and tuned by the Grid Search Cross Validation. Results: The highest accuracy occurred when using features from PADEL, CDK, RDKIT, MORDRED, BLUEDESC combinations, resulting in an accuracy of 80%. Conclusion: Our proposed model outperforms previous studies in accuracy categories. The information and source code is accessible online at Github: https://github.com/Osphanie/Thrombocytopenia