ADMET-Informed Machine Learning Prioritization of Hydrogel-Associated Compounds for Nanocarrier Evaluation
Abstract
Experimental screening of large hydrogel-associated chemical collections is slow and resource-intensive. We developed an ADMET-informed machine-learning workflow for 2060 HydrogelDB-linked compounds, using the median of a separately reported admetSAR3.0 biodegradability-related output to define higher- and lower-score classes and the remaining 115 predicted ADMET properties as model inputs; preprocessing, feature selection, hyperparameter optimization, probability calibration, and threshold selection were confined to the training partitions of nested cross-validation. The calibrated linear SVM achieved a mean held-out MCC of 0.9116 ± 0.0222, an F2-score of 0.9662 ± 0.0112, and a ROC–AUC of 0.9904 ± 0.0048; performance remained high after proxy-feature removal and feature-family ablation, whereas structure-focused models showed a weaker but detectable signal. Strict eligibility criteria retained 19 candidates. Six-component ranking under changes in model outputs and weights identified D-Alanyl-D-Alanine, Prolyl-Glutamate, and Pro-Asp as the most stable leading candidates. N-Glycylalanine showed lower ranking stability, with Isoleucylglycine as the main alternative. All shortlisted compounds require experimental validation of biodegradability, hydrogel formation, and nanocarrier performance.
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BibTeX
@misc{hosseinyazdi2026admetinformed,
title = {{ADMET-Informed Machine Learning Prioritization of Hydrogel-Associated Compounds for Nanocarrier Evaluation}},
author = {Zeinab Hosseinyazdi and Seyed Majid Hashemianzadeh},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15009753/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009753/v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - ADMET-Informed Machine Learning Prioritization of Hydrogel-Associated Compounds for Nanocarrier Evaluation
AU - Zeinab Hosseinyazdi
AU - Seyed Majid Hashemianzadeh
PY - 2026
DA - 2026/10/01
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009753/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009753/v1
AB - Experimental screening of large hydrogel-associated chemical collections is slow and resource-intensive. We developed an ADMET-informed machine-learning workflow for 2060 HydrogelDB-linked compounds, using the median of a separately reported admetSAR3.0 biodegradability-related output to define higher- and lower-score classes and the remaining 115 predicted ADMET properties as model inputs; preprocessing, feature selection, hyperparameter optimization, probability calibration, and threshold selection were confined to the training partitions of nested cross-validation. The calibrated linear SVM achieved a mean held-out MCC of 0.9116 ± 0.0222, an F2-score of 0.9662 ± 0.0112, and a ROC–AUC of 0.9904 ± 0.0048; performance remained high after proxy-feature removal and feature-family ablation, whereas structure-focused models showed a weaker but detectable signal. Strict eligibility criteria retained 19 candidates. Six-component ranking under changes in model outputs and weights identified D-Alanyl-D-Alanine, Prolyl-Glutamate, and Pro-Asp as the most stable leading candidates. N-Glycylalanine showed lower ranking stability, with Isoleucylglycine as the main alternative. All shortlisted compounds require experimental validation of biodegradability, hydrogel formation, and nanocarrier performance.
ER -