INTEGRATING BIOINFORMATICS AND DEEP LEARNING FOR DRUG SIDE EFFECT PREDICTION

Abstract

This research investigates the integration of deep learning techniques and bioinformatics to enhance the precision and efficacy of pharmacological adverse effect forecasts. The objective of this investigation is to identify intricate patterns and concealed connections that result in adverse drug reactions by integrating advanced deep learning algorithms with bioinformatics data, including genetic information, protein interactions, and drug-target networks. In contrast to conventional statistical methodologies, the methodology utilizes a wide range of datasets to improve the accuracy of its forecasts. The objective of this research is to enhance the precision and reliability of side effect predictions, reduce risks in clinical trials, and advance personalized medicine by enhancing early pharmaceutical safety evaluation.