Bioinfo Chem
System biology and Infochemistry | Online ISSN 3071-4826
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Volume 3 Number 1 2021
REVIEWS (Open Access)
AI-Driven Prediction of ncRNA–Drug Interactions: From Computational Modeling to Precision Therapeutics: A review
Blessing A. Aderibigbe 1*, Vuyolwethu Khwaza 1*
Bioinfo Chem 3 (1) 1-16 https://doi.org/10.25163/bioinformatics.3110735
Submitted: 25 February 2021 Revised: 10 April 2021 Accepted: 19 April 2021 Published: 21 April 2021
Abstract
Drug discovery, if one reflects on its trajectory, has long been anchored in a protein-centric paradigm—effective, certainly, yet increasingly constrained by the reality that a vast proportion of disease-relevant targets remain pharmacologically inaccessible. In parallel, advances in genomics have quietly reshaped this landscape, revealing non-coding RNAs (ncRNAs) not as passive transcriptional artifacts, but as central regulators of gene expression and disease progression. This review explores the evolving intersection between ncRNA biology and artificial intelligence (AI)-driven computational modeling, focusing on the prediction of ncRNA–drug interactions as a pathway toward precision therapeutics. We examine classical approaches, including structure-based docking, and contrast them with emerging machine learning and deep learning frameworks such as graph convolutional networks and sequence-based architectures. These models, while not without limitations, demonstrate an increasing capacity to infer complex interaction patterns even in the absence of complete structural data. Particular attention is given to data ecosystems, network-based representations, and hybrid modeling strategies that integrate biological, chemical, and transcriptomic information. Yet, the field remains marked by uncertainty—data sparsity, interpretability challenges, and validation gaps persist. Still, there is a cautious optimism. As computational tools become more adaptive and biological insights deepen, AI-driven ncRNA–drug prediction may not merely complement traditional pharmacology but redefine it.
Keywords: Non-coding RNA (ncRNA); AI-driven drug discovery; ncRNA–drug interactions; Graph neural networks; Precision therapeutics
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