Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery

ABSTRACT

Introduction

Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality.

Areas covered

The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs.

Expert opinion

Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA – small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.

KEYWORDS:

• Artificial intelligence
• deep learning
• hit-to-lead optimization
• machine learning
• RNA structure
• RNA-targeted small molecule drugs
• target identification

Article highlights

• Artificial intelligence (AI) tools can be used for generating hypotheses on the role of RNAs in disease.

• Recent machine learning and deep learning approaches have shown improved accuracy in predicting RNA secondary and tertiary structures, as well as in detecting small molecule binding sites on RNAs.

• Applications of AI-based approaches in virtual screening against RNA targets and the design of RNA-focused libraries have emerged and are expected to boost the discovery of novel RNA binders/modulators.

• Although the applications are still limited, it is clear that AI will be integral to quantitative structure – activity/property relationship modeling and de novo drug design.

• AI approaches to improve the accuracy of molecular dynamics simulations and that combine rule-based and data-driven systems, data and algorithm sharing, and the generation vast amounts of high-quality data will accelerate the discovery of novel RNA-targeted small molecule drugs.

Declaration of interest

EC Morishita is a senior investigator at Veritas In Silico, Inc. S Nakamura is the scientific founder and CEO of Veritas In Silico, Inc. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

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

Additional information

Funding

This paper was not funded.