Artificial intelligence (AI) has evolved significantly over the past decade, but it is still not clear whether these advanced computational tools can live up to the buzz. Within drug development, specifically, toxicology is one area where AI systems have the potential to revolutionize the process. By applying AI to a variety of workflows, researchers can not only predict potential toxicity or off-target effects but also process the mountains of data generated from experimental analysis in a quick and efficient way that is meaningful and free of human bias. AI and machine learning (ML) algorithms may allow scientists to gain a deeper understanding of the mechanisms related to toxicity and subtle differences in the the genetic makeup that influence the maximum tolerated dose or efficacy of drug in different individuals. Ultimately, AI can speed up pre-clinical development, save companies immense amounts of time and money, and get to market quicker.
This webinar will take a closer look at the use and success of applications of AI and ML in drug development and best practices around implementation. We will tackle the big questions, including: