Dataset and Code: Formulation-Aware QSAR for Inhaled Neuraminidase Inhibitors with Explainable AI

This repository contains the curated dataset, trained model predictions, analysis code, and publication-quality figures for the manuscript: Noorizadehtehrani, S.; Formulation-Aware QSAR for Inhaled Neuraminidase Inhibitors: Integrating Aerodynamic Descriptors into Predictive Models with Explainable Artificial Intelligence. J. Chem. Inf. Model. 2026. Contents: Curated ChEMBL dataset (1,944 neuraminidase inhibitors with 30 molecular descriptors) Model predictions (Random Forest, Gradient Boosting, SVR) QSAR training code and figure generation scripts All manuscript figures (PNG + TIFF) Best model: Random Forest (R² = 0.550, RMSE = 1.007, 5-fold CV R² = 0.589 ± 0.036)