# A Multi-Model Machine Learning Framework for Identifying Raloxifene as a Novel RNA Polymerase Inhibitor from FDA-Approved Drugs

## TL;DR

Researchers used machine learning and molecular simulations to identify raloxifene, an FDA-approved drug, as a potential RNA polymerase inhibitor for antiviral use.

## Contribution

A novel multi-model machine learning framework was developed to repurpose FDA-approved drugs as RNA polymerase inhibitors.

## Key findings

- Raloxifene showed binding affinity comparable to a known inhibitor in molecular docking studies.
- Molecular dynamics simulations confirmed stable binding and hydrogen bonding patterns of raloxifene with the RdRP.
- The machine learning models achieved high accuracy and specificity in identifying potential RdRP inhibitors.

## Abstract

RNA-dependent RNA polymerase (RdRP) represents a critical target for antiviral drug development. We developed a multi-model machine learning framework combining five traditional algorithms (ExtraTreesClassifier, RandomForestClassifier, LGBMClassifier, BernoulliNB, and BaggingClassifier) with a CNN deep learning model to identify potential RdRP inhibitors among FDA-approved drugs. Using the PubChem dataset AID 588519, our ensemble models achieved the highest performance with accuracy, ROC-AUC, and F1 scores higher than 0.70, while the CNN model demonstrated complementary predictive value with a specificity of 0.77 on external validation. Molecular docking studies with the norovirus RdRP (PDB: 4NRT) identified raloxifene as a promising candidate, with a binding affinity (−8.8 kcal/mol) comparable to the positive control (−9.2 kcal/mol). The molecular dynamics simulation confirmed stable binding with RMSD values of 0.12–0.15 nm for the protein–ligand complex and consistent hydrogen bonding patterns. Our findings suggest that raloxifene may possess RdRP inhibitory activity, providing a foundation for its experimental validation as a potential broad-spectrum antiviral agent.