supplementary material from Computational Discovery of TGF-β R1 inhibitors from the EMNPD database via machine learning, molecular docking and steered molecular dynamics

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Authors: Thuy Phan, Ha Nguyen Xuan, Duc Dau Xuan
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Abstract

The datasets supporting this article have been uploaded as part of the Supplementary Material. Specifically, Table S1 list of EMNPD compounds with ML-predicted binding free energies and training and test datasets. Figures S1–S5 include: (i) validation of the molecular docking protocol via redocking of co-crystallized ligands (Figure S1); (ii) 2D interaction diagrams between ligands and TGF-β1 protein structures (Figure S2); (iii) RMSD plots from 100 ns molecular dynamics simulations of TGFβR1–ligand complexes (Figure S3); (iv) steered molecular dynamics (SMD) profiles—force–time and work–time—for multiple protein–ligand complexes with PDB IDs 1VJY, 2WOT, 2WOU, 2X7O, 3FAA, 5QIM, 5QTZ, 5QU0, and 5USQ (Figure S4); and (v) SMD analyses of selected ligands with TGFβR1 (Figure S5).

Fields of Study & Subjects

TGF-β signaling in diseases Machine Learning in Bioinformatics Computational Drug Discovery Methods

Publication details

Journal / source
Figshare
Publication date
1 January 2026