AI-Driven, Animal-Test–Free Drug Discovery

Abstract

Artificial intelligence (AI) has become a game-changer for pharmaceutical research and opens up new ways to replace or cut down on animal testing in drug development. When using AI-driven computational modeling, molecular simulations, and data from organ-on-chip methods, it is possible to accurately predict drug efficacy and produce very few false positive drug leads. Drug-target interaction models use graph neural networks (GNNs) and transformer-based molecular models. These are the basic building blocks of generative AI for DES. It also makes new compounds that are best for in-vivo pharmacokinetics and safety in the environment. Additionally, the integration of AI with in vitro and in silico systems enables the execution of virtual clinical studies to improve predictive accuracy and mitigate ethical dilemmas. Recent research indicates that frameworks operating under AI control for drug development can reduce preclinical testing durations by over 60%, thereby decreasing costs and adhering to the 3Rs principle of biomedical ethics (Replacement, Reduction, and Refinement). This study shows that AI methods can help us get to a drug discovery model that doesn't use animals and is cheaper and more sustainable. If we go this way, we could save 2 trillion US dollars a year in just 20 years.