[Drug Discovery - A surge in innovation driven by technology and artificial intelligence]

Historically, the discovery of new pharmaceutical therapies has constituted a lengthy and cost-intensive process. Between the initial identification of a drug candidate and successful regulatory approval, 10 to 15 years commonly elapse, with development costs reaching the order of billions of dollars. Research activities, which are still largely empirical in nature, are frequently marked by setbacks, for example due to toxicological findings or insufficient efficacy in preclinical disease models. Technological breakthroughs, such as the sequencing of the human genome, have given rise to largely unmet expectations that medicines could be developed more rapidly and efficiently. Accordingly, expectations for the application of artificial intelligence (AI) in pharmaceutical research and development are high. Indeed, workflows in the preclinical phase are currently being fundamentally transformed by AI. In the following, the individual stages of preclinical research are analysed, and the potential applications of AI are described. In addition, selected critical aspects are discussed to realistically contextualize expectations regarding AI. As a result of the integration of AI, both preclinical and clinical research are undergoing profound transformation. There is justified hope that effective and safe therapies can finally be developed more rapidly and cost-efficiently.