ArticleMolecular diversity2026
Computational design of MMP-13 inhibitors using a combined approach of machine learning, docking, and molecular dynamics.
Article in Molecular diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- In silico prioritization of potential matrix metalloproteinase-13 inhibitors for cartilage degeneration in knee injury-associated osteoarthritis.BMC biotechnology · 2026Article
- Molecular Dynamics and Experimental Validation of Natural Products from Chuanxiong Rhizoma as VEGFR2 Inhibitors for nAMD Therapy.ACS omega · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Matrix metalloproteinase-13 (MMP-13) is a zinc-dependent endopeptidase involved in extracellular matrix degradation and inflammation, contributing to the progression of various diseases. This study applied an integrated computational approach encompassing QSAR modeling, machine learning (ML), scaffold analysis, docking, and molecular dynamics (MD) simulations to investigate the structure-activity relationships and binding mechanisms of MMP-13 inhibitors. A curated dataset of 1,741 unique compounds from ChEMBL was used to develop predictive QSAR models based on PubChem fingerprints. Among eight regression models, LGBM, SVR, and RF exhibited superior predictive performance, with LGBM achieving the best generalization (test RMSE = 0.825, R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.