ArticleInternational journal of molecular sciences2014
3D QSAR studies, pharmacophore modeling and virtual screening on a series of steroidal aromatase inhibitors.
Article in International journal of molecular sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
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- Development of Potential Multi-Target Inhibitors for Human Cholinesterases and Beta-Secretase 1: A Computational Approach.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Molecular Multi-Target Approach for Human Acetylcholinesterase, Butyrylcholinesterase andPharmaceuticals (Basel, Switzerland) · 2023Article
- Finding the molecular scaffold of nuclear receptor inhibitors through high-throughput screening based on proteochemometric modelling.Journal of cheminformatics · 2018Article
- Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.International journal of molecular sciences · 2018Article
- 3D-QSAR study of steroidal and azaheterocyclic human aromatase inhibitors using quantitative profile of protein-ligand interactions.Journal of cheminformatics · 2018Article
- Towards understanding aromatase inhibitory activity via QSAR modeling.EXCLI journal · 2018Review
- Identification of potential aryl hydrocarbon receptor ligands by virtual screening of industrial chemicals.Environmental science and pollution research international · 2018Article
- Pharmacophore Based Virtual Screening Approach to Identify Selective PDE4B Inhibitors.Iranian journal of pharmaceutical research : IJPR · 2017Article
- A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors.International journal of molecular sciences · 2015Article
- Correction: Xie, H.; et al. 3D QSAR studies, pharmacophore modeling and virtual screening on a series of steroidal aromatase inhibitors. Int. J. Mol. Sci. 2014, 15, 20927-20947.International journal of molecular sciences · 2015Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent steroidal aromatase inhibitors (SAIs) with lower side effects and overcome cellular resistance, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of SAIs to build 3D QSAR models. The reliable and predictive CoMFA and CoMSIA models were obtained with statistical results (CoMFA: q² = 0.636, r²(ncv) = 0.988, r²(pred) = 0.658; CoMSIA: q² = 0.843, r²(ncv) = 0.989, r²(pred) = 0.601). This 3D QSAR approach provides significant insights that can be used to develop novel and potent SAIs. In addition, Genetic algorithm with linear assignment of hypermolecular alignment of database (GALAHAD) was used to derive 3D pharmacophore models. The selected pharmacophore model contains two acceptor atoms and four hydrophobic centers, which was used as a 3D query for virtual screening against NCI2000 database. Six hit compounds were obtained and their biological activities were further predicted by the CoMFA and CoMSIA models, which are expected to design potent and novel SAIs.
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