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pendrin-qsar: An Open-Source QSAR Pipeline for Pendrin (SLC26A4) Inhibitors

Year: 2026

Type: article

Abstract: An open-source cheminformatics and machine learning pipeline for pendrin (SLC26A4) inhibitors. Includes a curated dataset of 16 unique inhibitors across 6 scaffold classes, four QSAR models (Random Forest, XGBoost, Ridge, PLS) validated by LOO-CV, Y-scrambling, applicability domain analysis, and conformal prediction, plus virtual screening of 13 novel benzofuran analogs. Top predicted virtual screening hit: V-2F4Cl (predicted IC50 = 0.86 µM, 80% CI: 0.43-1.73 µM). Code runs in a single click in Google Colab with no installation required. GitHub: https://github.com/drjoykarmakar/pendrin-qsar

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.19534440

Author: Karmakar, Joy

Institution: University of California, San Francisco

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7153829798

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