From Lab to Counter: The Impact of AI on Drug Discovery & Drug development

Abstract

Drug Discovery and Development is a tedious and complicated process which involves a series of steps starting from research, identification and testing of lead molecules to final regulatory approval. Implying artificial intelligence in all these tasks doesn't only reduce cost to a greater extent but also saves time and resources. Using AI in healthcare sector has significantly aided in transforming it by using newer technologies and innovations. The process which was earlier laborious and lengthy were impacted positively by AI. The machine learning models were provided with data and information which made it easier to predict the characteristics and properties of drug molecules such as sar, shelf life, toxic concentrations, contraindications and incompatiblity hypothetically even before the molecules were invented. The lifecycle of drug discovery and development include Drug Discovery, Preclinical and Clinical Trials, Manufacturing and Formulation Development and Dispensing. While drug discovery using AI determined which drug molecule can be efficient for further research, preclinical and clinical trials provides overall information about it's effect, effective dose, lethal dose, side effects as well as pharmacodynamic and pharmacokinetic parameters. Thus, the drug passing the clinical trials phase is then considered as effective, stable, safe and is then used to manufacture different formulations. The task of dispensing needs to be precise and it may cause serious after effects of errors occur.While there are a few risks, the advantages always have an upperhand. To minimize these errors Artificial Intelligence algorithms and Machine Learning models work effectively in conjunction to provide an overall better experience.