Data and analysis outputs for: A Scaffold-Aware QSAR and Docking Workflow for Rational Compound Prioritization in Structure-Based Virtual Screening

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Authors: Tomoyuki Koguchi
Cited by: 0

Abstract

This Zenodo record provides curated datasets and analysis outputs supporting the manuscript entitled “A Scaffold-Aware QSAR and Docking Workflow for Rational Compound Prioritization in Structure-Based Virtual Screening.” All analyses were performed using reproducible Python-based workflows. The deposited files enable full reproduction of the reported QSAR modeling, scaffold-based data splitting, docking validation, Y-randomization, and applicability domain analyses. The record is publicly visible, and the files are currently under restricted access for peer review. Full access will be granted upon publication.

Fields of Study & Subjects

Computational Drug Discovery Methods Machine Learning in Materials Science Synthesis and Characterization of Heterocyclic Compounds

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
9 February 2026
Language
EN
Document type
Dataset