In situ development of an artificial intelligence (AI) model for early detection of adverse drug reactions (ADRs) to ensure drug safety
Abstract
Pharmacovigilance is a vital component of public health systems, aiming to ensure the safe use of medicinal products. In this study, an artificial intelligence (AI)-based model was developed using TensorFlow to predict the likelihood of adverse drug reactions (ADRs) based on molecular structure and predefined criteria. Data from DrugBank, MedDRA, and SIDER databases were extracted, integrated, and structured in a relational model. A feedforward neural network was trained using chemical and pharmacological descriptors such as SMILES and ATC codes. The model showed consistent performance in estimating ADR risk, highlighting the potential role of AI in supporting early safety assessments. This method may enhance post-marketing surveillance through more timely and data-driven risk identification. Despite certain limitations, AI-assisted modeling represents a valuable addition to pharmacovigilance and patient safety awareness strategies.
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