Advances in the discovery of new chemotypes through ultra-large library docking. (4th March 2023)

• Record Type: Journal Article
• Authors: Potlitz, Felix; Link, Andreas; Schulig, Lukas
• Is Part Of: Expert opinion on drug discovery. Volume 18:Number 3(2023)
• Page Start: 303
• Page End: 313
• Publication Date: 2023-03-04
• Subjects: Drug discovery -- virtual screening -- protein-ligand docking -- large-scale ligand libraries -- deep-learning -- SARS-CoV-2; 615.1
• DOI: 10.1080/17460441.2023.2171984
• Languages: English
• ISSNs: 1746-0441

Abstract

ABSTRACT: Introduction: The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (M pro ). Areas covered: This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel M pro inhibitors targeting the SARS-CoV-2 virus. Expert opinion: With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.