EventsMOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
Published
This submission belongs to the session 01. CHEMBIO.INFO-09: Cheminfo., Chemom., Comput. Quantum Chem. & Bioinfo. Congress München, GR-Chapel Hill, USA, 2023. of the event MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
Published date
13 Apr, 2023
Academic Editor
author-avatarMOL2NET Team
Citation
Andrea Ruiz Escudero, Short Review AI-Driven Tools and Methods for Small Molecule Ligand Discovery and Prediction for RNA Interactions, in Proceedings of MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed., 25 December–31 December 2023, MDPI: Basel, Switzerland
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Short Review AI-Driven Tools and Methods for Small Molecule Ligand Discovery and Prediction for RNA Interactions

1. Department of Pharmacology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Leioa, Biscay, Spain.
2. Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña,Campus de Elviña, A Coruña, Spain.
Abstract

RNA molecules are crucial in many biological processes, therefore, they have become potential targets for disease diagnosis and treatment. The design of small molecules that can target RNA structures is a promising approach, as they are tunable and easily taken up by cells. However, it can be challenging without knowing the RNA structure. In this short opinion letter three different examples will be discussed of research groups combining AI to predict the interactions between RNA molecules and small molecules to address the challenges in designing RNA-targeted ligands due to the difficulty in obtaining accurate RNA structures and the lack of understanding of binding kinetics.

Keywords
Artificial Intelligence
computational modeling
RNA
ligands
RNA interactions
small molecules
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