A DEEP LEARNING FRAMEWORK FOR PREDICTING ADVERSE DRUG REACTIONS USING MULTI-MODAL BIOMEDICAL DATA

Abstract

The prediction of adverse drug reactions (ADRs) remains a significant challenge in modern pharmacovigilance, as the increasing complexity of drug compounds and their interactions with human biological systems can lead to severe side effects. This research presents a deep learning framework that integrates multi-modal biomedical data, including molecular descriptors, drug–target interactions, and clinical records, to predict potential ADRs before clinical manifestation. By employing a hybrid architecture combining convolutional neural networks (CNNs) for feature extraction and long short-term memory (LSTM) networks for temporal dependency modeling, the proposed system enhances predictive accuracy compared to conventional machine learning methods. Experimental evaluation using benchmark pharmacovigilance datasets demonstrates superior performance in terms of precision, recall, and F1-score, indicating the model’s capability to effectively identify complex drug–effect relationships. This approach holds promise for improving drug safety assessment and supporting data-driven decision-making in pharmaceutical research.