3D-QSAR and molecular docking studies of 2-pyrimidinecarbonitrile derivatives as inhibitors against falcipain-3

Potshangbam, Angamba Meetei; Tanneeru, Karunakar; Reddy, B. Madhusudhan; Guruprasad, Lalitha

doi:10.1016/j.bmcl.2011.09.107

Cited by 20 publications

(14 citation statements)

References 26 publications

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“…Quantitative structure–activity relationships (QSARs) have been successfully applied in the study of the relationship between physicochemical properties of chemical substances and their biological activities to obtain a supportive statistical model for predicting the activity of new chemical entities. 3D‐QSAR has also emerged as a natural extension to the classical Hansch and Free‐Wilson approach, exploiting the three‐dimensional properties of the ligands to predict their biological activities using chemometric techniques such as partial least squares (PLS), genetic partial least squares (G/PLS) and artificial neural network (ANN) . What is more, 3D‐QSAR has served as a valuable predictive tool in the design of novel drugs.…”

Section: Introductionmentioning

confidence: 99%

3D-quantitative structure–activity relationship and docking studies of coumarin derivatives as tissue kallikrein 7 inhibitors

Zheng

Tan

et al. 2017

Journal of Pharmacy and Pharmacology

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Section: Introductionmentioning

confidence: 99%

3D-quantitative structure–activity relationship and docking studies of coumarin derivatives as tissue kallikrein 7 inhibitors

Zheng

Tan

et al. 2017

Journal of Pharmacy and Pharmacology

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“…Since static strings are statistically similar and inefficient, hence they do not contribute to the model building. Only electrostatic and static descriptors contributed to the model generation (Potshangbam et al, 2011). Partial least square (PLS) regression analysis along with stepwise forward variable selection method was applied to build the 3D-QSAR model.…”

Section: Resultsmentioning

confidence: 99%

Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach

et al. 2017

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Spinocerebellar ataxia (SCA-2) type-2 is a rare neurological disorder among the nine polyglutamine disorders, mainly caused by polyQ (CAG) trinucleotide repeats expansion within gene coding ataxin-2 protein. The expanded trinucleotide repeats within the ataxin-2 protein sequesters transcriptional cofactors i.e., CREB-binding protein (CBP), Ataxin-2 binding protein 1 (A2BP1) leading to a state of hypo-acetylation and transcriptional repression. Histone de-acetylases inhibitors (HDACi) have been reported to restore transcriptional balance through inhibition of class IIa HDAC's, that leads to an increased acetylation and transcription as demonstrated through in-vivo studies on mouse models of Huntington's. In this study, 61 di-aryl cyclo-propanehydroxamic acid derivatives were used for developing three dimensional (3D) QSAR and pharmacophore models. These models were then employed for screening and selection of anti-ataxia compounds. The chosen QSAR model was observed to be statistically robust with correlation coefficient (r2) value of 0.6774, cross-validated correlation coefficient (q2) of 0.6157 and co-relation coefficient for external test set (pred_r2) of 0.7570. A high F-test value of 77.7093 signified the robustness of the model. Two potential drug leads ZINC 00608101 (SEI) and ZINC 00329110 (ACI) were selected after a coalesce procedure of pharmacophore based screening using the pharmacophore model ADDRR.20 and structural analysis using molecular docking and dynamics simulations. The pharmacophore and the 3D-QSAR model generated were further validated for their screening and prediction ability using the enrichment factor (EF), goodness of hit (GH), and receiver operating characteristics (ROC) curve analysis. The compounds SEI and ACI exhibited a docking score of −10.097 and −9.182 kcal/mol, respectively. An evaluation of binding conformation of ligand-bound protein complexes was performed with MD simulations for a time period of 30 ns along with free energy binding calculations using the g_mmpbsa technique. Prediction of inhibitory activities of the two lead compounds SEI (7.53) and ACI (6.84) using the 3D-QSAR model reaffirmed their inhibitory characteristics as potential anti-ataxia compounds.

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“…The model has a high R 2 (0.980 and 0.986 for series 4 and 5 respectively see in Fig 6) with a low standard deviation(SE, 0.097 and 0.117, respectively) and a high Fischer ratio (F, 116.544 and 164.602, respectively); while a QSAR model is generally acceptable if R 2 is approximately 0.9 or higher [33]. Specially, the cross-validation related coefficient q 2 is 0.696 and 0.564 respectively ( >0.5 ) [34], suggesting a good prediction ability of this model [34].…”

Section: D-qsarmentioning

confidence: 97%

Synthesis and quantitative structure–activity relationships study for phenylpropenamide derivatives as inhibitors of hepatitis B virus replication

Yang

Wang

et al. 2015

European Journal of Medicinal Chemistry

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3D-QSAR and molecular docking studies of 2-pyrimidinecarbonitrile derivatives as inhibitors against falcipain-3

Cited by 20 publications

References 26 publications

3D-quantitative structure–activity relationship and docking studies of coumarin derivatives as tissue kallikrein 7 inhibitors

3D-quantitative structure–activity relationship and docking studies of coumarin derivatives as tissue kallikrein 7 inhibitors

Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach

Synthesis and quantitative structure–activity relationships study for phenylpropenamide derivatives as inhibitors of hepatitis B virus replication

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