Predictive Modelling of Drug Side Effects using Bioinformatics and Machine Learning

Abstract: This article explores the integration of databases to compare and evaluate the machine learning algorithms that suits multi-label classification. In this predictive modelling work, I have performed Logistic regression, Random Forest, XGBoost, Multi-Layer Perceptron. Logistic Regression is for linear model prediction of drug side effects, Random Forest is chosen for High-Dimensional Biomedical datasets, XGBoost was selected because it is one of the most powerful gradient boosting algorithms, Multi-Layer Perceptron (MLP) architecture was used to learn nonlinear relationships between drug features and side-effect labels. As this is a predictive modelling task evaluation of machine learning models is essential step to assess its predictive capability, reliability, and generalization performance using appropriate evaluation metrics. The computational analysis for this study was carried out using Python due to its flexibility and strong ecosystem of scientific computing libraries. In this study, multiple machine learning models were implemented and compared to evaluate their effectiveness in predicting drug side effects using molecular properties and pharmacokinetics features.

Keywords: Adverse drug reactions, Machine learning, Logistic regression, Random forest, XGBoost, Multi-layer perceptron, Neural network, precision, recall.