ArticleRSC advances2023
Discovery of new STAT3 inhibitors as anticancer agents using ligand-receptor contact fingerprints and docking-augmented machine learning.
Article in RSC advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Oxidation-derived metabolites sustain the antioxidant network of quercetin.Journal of computer-aided molecular design · 2026Article
- Computer-aided discovery of CDK16 inhibitors: a docking-augmented machine learning regression modelling approach.Journal of computer-aided molecular design · 2026Article
- Lacosamide prevents cyclophosphamide-induced testicular dysfunction via inhibition of NF-κB/IL-6/STAT-3 and JNK1/Caspase-3 axes with AR and HO-1 preservation: in vivo and in silico evidence.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Unraveling the Mechanisms of Madecassoside Derivatives in Wound Healing: Network Pharmacology and Experimental Validation.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors.Scientific reports · 2024Article
- Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study.Journal of computer-aided molecular design · 2023Article
- Fluorinated andPharmaceutics · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STAT3 belongs to a family of seven vital transcription factors. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of STAT3 inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contact Fingerprints and scoring values were implemented as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to identify critical descriptors that determine anti-STAT3 bioactivity to be translated into pharmacophore model(s). Two successful pharmacophores were deduced and subsequently used for
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Registered trials
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