Artificial Intelligence in Pharmacology, Drug Safety and Toxicity

Abstract

Artificial intelligence (AI) is transforming pharmacology, drug safety, and toxicology by accelerating the drug development process to be more efficient, precise, and economical. Conventional drug discovery, pre-clinical testing, and post-marketing surveillance methods frequently encounter high costs, long lead times, ethical constraints, and low predictive validity in human outcomes. Utilizing machine learning (ML) and deep learning (DL), AI combines heterogenous datasets chemical structures, genomics, clinical data, and imaging to bridge these gaps.In drug design and discovery, AI has hastened predictions of protein and RNA structures (e.g., AlphaFold), enhanced virtual screening, and enabled de novo drug design with generative models. It has also hastened peptide-based drug development and improved pharmacokinetic prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) and reduced failure rates.