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Iris: A Machine Learning-based Pose Reranking Tool For Rna-ligand Docking.

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Iris: A Machine Learning-based Pose Reranking Tool For Rna-ligand Docking.

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IRIS: A Machine Learning-Based Pose Reranking Tool for RNA-Ligand Docking.

Jason Andrew Amburn¹, Shalini J Rukmani^2,3, Jerry M Parks^2,4

¹Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Tennessee 37996, United States.

|March 16, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Intelligent RNA Interaction Scorer (IRIS) improves RNA-ligand docking pose ranking. This machine learning model enhances accuracy for structure-based drug discovery targeting RNA molecules.

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Area of Science:

Computational chemistry
Structural biology
Drug discovery

Background:

RNA molecules are crucial in cellular processes and disease, making them key therapeutic targets.
Accurate prediction of RNA-ligand complex 3D structures via computational docking is vital for rational drug design.
RNA-ligand docking is challenging due to RNA's flexibility and charged backbone, and existing tools like rDock struggle with accurate pose ranking.

Purpose of the Study:

To develop and validate the Intelligent RNA Interaction Scorer (IRIS), a novel machine learning model to enhance RNA-ligand docking pose ranking.
To improve the accuracy of identifying near-native ligand poses for RNA targets in drug discovery pipelines.

Main Methods:

Developed IRIS, a regression model using physicochemical and interaction-based features.

Trained IRIS on the largest dataset of experimental nucleic acid-ligand complexes available for an ML-based RNA docking tool (608 structures).

Integrated IRIS with the rDock program to rerank ligand poses generated by rDock.

Main Results:

IRIS significantly improved rDock's RNA-ligand pose ranking accuracy compared to using rDock scores alone.
When using the best rDock protocol, IRIS increased the success rate of ranking a near-native pose within the top five from 64.6% to 78.0%.
IRIS improved the top-ranked pose accuracy, with the correct pose ranked first in 59.8% of cases, up from rDock's 42.7%.

Conclusions:

IRIS effectively enhances the accuracy of RNA-ligand docking pose ranking.
The model can be seamlessly integrated into existing docking workflows to improve RNA-targeted drug discovery.
IRIS offers a significant advancement for structure-based inhibitor design against RNA targets.