Computational Prediction of Adverse Drug Reactions and Toxicity Using AI and ML

Abstract

Adverse drug reactions (ADRs) and drug toxicity are serious problems in healthcare, threatening patient safety and driving up costs. Though they're not always as immediately obvious as infectious diseases, their consequences can be severe. Detecting these issues early is vital for understanding how safe and effective a drug truly is. Artificial intelligence (AI) and machine learning (ML) are revolutionizing this early detection. These technologies can quickly and accurately predict potential ADRs and toxicity risks long before a drug is even synthesized or enters preclinical and clinical testing. This review explores how AI and ML are used for this purpose, covering a wide range of methods from data mining to deep learning. We dive into the relevant databases, modeling algorithms, and software tools used for ADR and toxicity prediction. By highlighting what these technologies can do, we show their power to fundamentally change drug discovery and make treatments safer for patients. But AI's impact doesn't stop there. This review also looks at how AI is transforming ongoing drug monitoring in healthcare. By enabling real-time data analysis and continuous surveillance, AI helps improve how well drugs work and reduces harmful reactions. Its sophisticated algorithms can make sense of complex patient data, paving the way for personalized treatment plans and precision medicine. Furthermore, AI-driven monitoring systems help lower risks, minimize errors, and optimize patient care, leading to better health results. We also look ahead to AI's future in drug monitoring, considering important ethical and regulatory questions. Ultimately, AI is key to building a more efficient, personalized, and patient-focused healthcare system, promising to reshape how care is delivered and improve outcomes.