Advances in the discovery of new chemotypes through ultra-large library docking

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.

KEYWORDS:

• Drug discovery
• virtual screening
• protein-ligand docking
• large-scale ligand libraries
• deep-learning
• SARS-CoV-2

Article highlights

• The field of ultra-large library docking has reached a tipping point.

• Bottlenecks in drug-design associated with high costs and universally limited resources such as material and time can be overcome using ultra-large library docking.

• Chemical space itself is incredibly huge, customly defined areas of interest have to be charted, instead.

• Deep learning enables on-the-fly generation of virtual molecules with implicitly defined properties during exploration.

• AI can learn from initial docking results and avoiding the necessity to dock all library entries; iterative virtual cycles considerably reduce the amount of individual docking tasks.

This box summarizes key points contained in the article.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or material discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or mending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.