Artificial intelligence in early drug discovery enabling precision medicine. (2nd September 2021)

• Record Type: Journal Article
• Authors: Boniolo, Fabio; Dorigatti, Emilio; Ohnmacht, Alexander J.; Saur, Dieter; Schubert, Benjamin; Menden, Michael P.
• Is Part Of: Expert opinion on drug discovery. Volume 16:Number 9(2021)
• Page Start: 991
• Page End: 1007
• Publication Date: 2021-09-02
• Subjects: Artificial intelligence -- machine learning -- deep learning -- precision medicine -- patient stratification -- biomarker discovery -- drug repurposing -- vaccine design -- protein design -- small molecule design; 615.1
• DOI: 10.1080/17460441.2021.1918096
• Languages: English
• ISSNs: 1746-0441

Abstract

ABSTRACT: Introduction : Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence. Areas covered : In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design. Expert opinion : Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.