ArticleInternational journal of molecular sciences2011
Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) studies on α(1A)-adrenergic receptor antagonists based on pharmacophore molecular alignment.
Article in International journal of molecular sciences, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Article
- 3D-QSAR, Molecular Docking, Molecular Dynamics Simulation, and Pharmacokinetic Prediction of 1H-Pyrazolo[3,4-d]pyrimidine Derivatives as PI3Kδ Inhibitors.Current medicinal chemistry · 2026Article
- Importance of Computer-aided Drug Design in Modern Pharmaceutical Research.Current drug discovery technologies · 2025Review
- 3D-QSAR, Scaffold Hopping, Virtual Screening, and Molecular Dynamics Simulations of Pyridin-2-one as mIDH1 Inhibitors.International journal of molecular sciences · 2024Article
- Potent VEGFR-2 inhibitors for resistant breast cancer: a comprehensive 3D-QSAR, ADMET, molecular docking and MMPBSA calculation on triazolopyrazine derivatives.Frontiers in molecular biosciences · 2023Article
- Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches.Frontiers in pharmacology · 2022Article
- Rational identification of small molecules derived from 9,10-dihydrophenanthrene as potential inhibitors of 3CLStructural chemistry · 2022Article
- Docking-Based 3D-QSAR Studies for 1,3,4-oxadiazol-2-one Derivatives as FAAH Inhibitors.International journal of molecular sciences · 2021Article
- Machine learningRSC advances · 2021Article
- Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens.International journal of molecular sciences · 2020Review
- Prediction of Drug-Target Binding Kinetics by Comparative Binding Energy Analysis.ACS medicinal chemistry letters · 2018Article
- Discovery of High-Affinity Cannabinoid Receptors Ligands through a 3D-QSAR Ushered by Scaffold-Hopping Analysis.Molecules (Basel, Switzerland) · 2018Article
- 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.Oncotarget · 2017Article
- In silico screening for identification of pyrrolidine derivatives dipeptidyl peptidase-IV inhibitors using COMFA, CoMSIA, HQSAR and docking studies.In silico pharmacology · 2017Article
- Exploration of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one derivatives as JAK inhibitors using various in silico techniques.In silico pharmacology · 2017Article
- Synthesis and cytotoxic activity evaluation of novel arylpiperazine derivatives on human prostate cancer cell lines.Molecules (Basel, Switzerland) · 2014Article
- The three dimensional Quantitative Structure Activity Relationships (3D-QSAR) and docking studies of curcumin derivatives as androgen receptor antagonists.International journal of molecular sciences · 2012Article
- CoMFA, CoMSIA, HQSAR and Molecular Docking Analysis of Ionone-based Chalcone Derivatives as Antiprostate Cancer Activity.Indian journal of pharmaceutical sciencesArticle
- QSPR analysis of some agonists and antagonists of α-adrenergic receptors.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agentsArticle
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5 authors.
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Abstract
The α(1A)-adrenergic receptor (α(1A)-AR) antagonist is useful in treating benign prostatic hyperplasia, lower urinary tract symptoms, and cardiac arrhythmia. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed on a set of α(1A)-AR antagonists of N-aryl and N-nitrogen class. Statistically significant models constructed from comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were established based on a training set of 32 ligands using pharmacophore-based molecular alignment. The leave-oneout cross-validation correlation coefficients were q(2) (CoMFA) = 0.840 and q(2) (CoMSIA) = 0.840. The high correlation between the cross-validated/predicted and experimental activities of a test set of 12 ligands revealed that the CoMFA and CoMSIA models were robust (r(2) (pred) (/CoMFA) = 0.694; r(2) (pred) (/CoMSIA) = 0.671). The generated models suggested that electrostatic, hydrophobic, and hydrogen bonding interactions play important roles between ligands and receptors in the active site. Our study serves as a guide for further experimental investigations on the synthesis of new compounds. Structural modifications based on the present 3D-QSAR results may lead to the discovery of other α(1A)-AR antagonists.
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