PAD4-DB: A Curated Structure–Activity Resource Reveals Hub-Organized Activity Cliffs and Scaffold-Dependent SAR Ruggedness in PAD4 Inhibitors
4 affiliations
- 1Laboratory of Enzyme Engineering and Microbiology, Engineering National School of Sfax (ENIS), University of Sfax, P.O. Box 1173, Sfax 3038, Tunisia
- 2Laboratory of Biochemistry and Enzymatic Engineering of Lipases, Engineering National School of Sfax (ENIS), University of Sfax, Sfax 3038, Tunisia
- 3Department of Chemical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
- 4Research Laboratory of Environmental Toxicology-Microbiology and Health (LR17ES06), Faculty of Sciences, University of Sfax, Sfax 3038, Tunisia
Abstract
Peptidylarginine deiminase 4 (PAD4) is an increasingly prominent therapeutic target in oncology, inflammatory disease, and neutrophil extracellular trap (NET)-associated pathologies, yet public bioactivity data for PAD4 inhibitors remain fragmented across multiple repositories with substantial redundancy and inconsistent annotation. Here, we present PAD4-DB, a curated structure–activity relationship resource integrating 3093 unique inhibitors (consensus pIC50 range 2.00–8.52; median 6.84) from PubChem, ChEMBL, and BindingDB through a reproducible pipeline encompassing structure standardization, activity normalization, source-independence assessment, and deduplication. Quantitative analysis of 358,416 compound pairs with Tanimoto similarity ≥0.6 indicates that the sampled PAD4 SAR landscape is predominantly smooth: only 94 pairs meeting the stringent activity-cliff criterion (Tanimoto ≥ 0.8; |ΔpIC50| ≥ 2.0) were identified, representing 0.026% of all related pairs and 0.78% of the 12,071 cliff-candidate pairs. Of these, 80 (85.1%) received structural support from matched molecular pair (MMP) analysis. Severe cliffs were non-randomly distributed, with four hub compounds collectively accounting for 53.2% of all severe cliff pairs, while 96.8% of multi-member scaffold series remained completely smooth. Provenance analysis further showed that 82.9% of the dataset was classified as pipeline-dependent under the provenance-scoring framework across repositories rather than independent source measurements, underscoring the importance of provenance-aware confidence weighting in downstream modeling. PAD4-DB therefore provides a reproducible foundation for PAD4 inhibitor discovery and a curated benchmark for evaluating similarity-based and machine-learning approaches in a chemically structured SAR landscape containing rare but highly concentrated activity cliffs.
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BibTeX
@article{tarhouni2026pad4db,
title = {{PAD4-DB: A Curated Structure–Activity Resource Reveals Hub-Organized Activity Cliffs and Scaffold-Dependent SAR Ruggedness in PAD4 Inhibitors}},
author = {Nidhal Tarhouni and Ahmed Bayoudh and Amira Mahfoudhi and Bilel Hadrich and Karim Kriaa and Imen Kallel},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = sep,
doi = {10.3390/ijms27188302},
url = {https://www.mdpi.com/1422-0067/27/18/8302},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - PAD4-DB: A Curated Structure–Activity Resource Reveals Hub-Organized Activity Cliffs and Scaffold-Dependent SAR Ruggedness in PAD4 Inhibitors
AU - Nidhal Tarhouni
AU - Ahmed Bayoudh
AU - Amira Mahfoudhi
AU - Bilel Hadrich
AU - Karim Kriaa
AU - Imen Kallel
PY - 2026
DA - 2026/09/17
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27188302
UR - https://www.mdpi.com/1422-0067/27/18/8302
AB - Peptidylarginine deiminase 4 (PAD4) is an increasingly prominent therapeutic target in oncology, inflammatory disease, and neutrophil extracellular trap (NET)-associated pathologies, yet public bioactivity data for PAD4 inhibitors remain fragmented across multiple repositories with substantial redundancy and inconsistent annotation. Here, we present PAD4-DB, a curated structure–activity relationship resource integrating 3093 unique inhibitors (consensus pIC50 range 2.00–8.52; median 6.84) from PubChem, ChEMBL, and BindingDB through a reproducible pipeline encompassing structure standardization, activity normalization, source-independence assessment, and deduplication. Quantitative analysis of 358,416 compound pairs with Tanimoto similarity ≥0.6 indicates that the sampled PAD4 SAR landscape is predominantly smooth: only 94 pairs meeting the stringent activity-cliff criterion (Tanimoto ≥ 0.8; |ΔpIC50| ≥ 2.0) were identified, representing 0.026% of all related pairs and 0.78% of the 12,071 cliff-candidate pairs. Of these, 80 (85.1%) received structural support from matched molecular pair (MMP) analysis. Severe cliffs were non-randomly distributed, with four hub compounds collectively accounting for 53.2% of all severe cliff pairs, while 96.8% of multi-member scaffold series remained completely smooth. Provenance analysis further showed that 82.9% of the dataset was classified as pipeline-dependent under the provenance-scoring framework across repositories rather than independent source measurements, underscoring the importance of provenance-aware confidence weighting in downstream modeling. PAD4-DB therefore provides a reproducible foundation for PAD4 inhibitor discovery and a curated benchmark for evaluating similarity-based and machine-learning approaches in a chemically structured SAR landscape containing rare but highly concentrated activity cliffs.
SN - 1422-0067
ER -