Dataset Information

3D Pharmacophore Model-Assisted Discovery of Novel CDC7 Inhibitors.

ABSTRACT: A ligand-based 3D pharmacophore model for serine/threonine kinase CDC7 inhibition was created and successfully applied in the discovery of novel 2-(heteroaryl)-6,7-dihydrothieno[3,2-c]pyridin-4(5H)-ones. The pharmacophore model provided a hypothesis for lead generation missed by docking to a homology model. Medicinal chemistry exploration of the series revealed clear structure-activity relationships consistent with the pharmacophore model and pointed to further optimization opportunities.

SUBMITTER: Lindvall M

PROVIDER: S-EPMC4018153 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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3D Pharmacophore Model-Assisted Discovery of Novel CDC7 Inhibitors.

Lindvall Mika M McBride Christopher C McKenna Maureen M Gesner Thomas G TG Yabannavar Asha A Wong Kent K Lin Song S Walter Annette A Shafer Cynthia M CM

ACS medicinal chemistry letters 20110802 10

A ligand-based 3D pharmacophore model for serine/threonine kinase CDC7 inhibition was created and successfully applied in the discovery of novel 2-(heteroaryl)-6,7-dihydrothieno[3,2-c]pyridin-4(5H)-ones. The pharmacophore model provided a hypothesis for lead generation missed by docking to a homology model. Medicinal chemistry exploration of the series revealed clear structure-activity relationships consistent with the pharmacophore model and pointed to further optimization opportunities. ...[more]

PMID: 24900258

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Project description:BackgroundRenin has become an attractive target in controlling hypertension because of the high specificity towards its only substrate, angiotensinogen. The conversion of angiotensinogen to angiotensin I is the first and rate-limiting step of renin-angiotensin system and thus designing inhibitors to block this step is focused in this study.MethodsLigand-based quantitative pharmacophore modeling methodology was used in identifying the important molecular chemical features present in the set of already known active compounds and the missing features from the set of inactive compounds. A training set containing 18 compounds including active and inactive compounds with a substantial degree of diversity was used in developing the pharmacophore models. A test set containing 93 compounds, Fischer randomization, and leave-one-out methods were used in the validation of the pharmacophore model. Database screening was performed using the best pharmacophore model as a 3D structural query. Molecular docking and density functional theory calculations were used to select the hit compounds with strong molecular interactions and favorable electronic features.ResultsThe best quantitative pharmacophore model selected was made of one hydrophobic, one hydrogen bond donor, and two hydrogen bond acceptor features with high a correlation value of 0.944. Upon validation using an external test set of 93 compounds, Fischer randomization, and leave-one-out methods, this model was used in database screening to identify chemical compounds containing the identified pharmacophoric features. Molecular docking and density functional theory studies have confirmed that the identified hits possess the essential binding characteristics and electronic properties of potent inhibitors.ConclusionA quantitative pharmacophore model of predictive ability was developed with essential molecular features of a potent renin inhibitor. Using this pharmacophore model, two potential inhibitory leads were identified to be used in designing novel and future renin inhibitors as antihypertensive drugs.

Dataset Information

3D Pharmacophore Model-Assisted Discovery of Novel CDC7 Inhibitors.

Publications

3D Pharmacophore Model-Assisted Discovery of Novel CDC7 Inhibitors.

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