Exploring QSARs for inhibitory effect of a set of heterocyclic thrombin inhibitors by multilinear regression refined by artificial neural network and molecular docking simulations

Ramírez-Galicia, Guillermo; Garduño‐Juárez, Ramón; Correa‐Basurto, José; Deeb, Omar

doi:10.3109/14756366.2011.582467

Cited by 11 publications

(8 citation statements)

References 45 publications

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“…Training models were built using the stepwise MLR subroutine of the spss for Windows 13.0 (2004) under the stepwise procedure. Multiple linear regression analysis with stepwise selection and elimination of variables was used to model the relations of the binding affinity pKi with each set of descriptors by applying the individual approach previously described .…”

Section: Methodsmentioning

confidence: 99%

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Exploring the Ligand Recognition Properties of the Human Vasopressin V1a Receptor Using QSAR and Molecular Modeling Studies

Contreras-Romo¹,

Martínez‐Archundia

Deeb

et al. 2013

Chem Biol Drug Des

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Vaptans are compounds that act as non-peptide vasopressin receptor antagonists. These compounds have diverse chemical structures. In this study, we used a combined approach of protein folding, molecular dynamics simulations, docking, and quantitative structure-activity relationship (QSAR) to elucidate the detailed interaction of the vasopressin receptor V1a (V1aR) with some of its blockers (134). QSAR studies were performed using MLR analysis and were gathered into one group to perform an artificial neural network (ANN) analysis. For each molecule, 1481 molecular descriptors were calculated. Additionally, 15 quantum chemical descriptors were calculated. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. Molecular modeling enabled us to obtain a reliable tridimensional model of V1aR. The docking results indicated that the great majority of ligands reach the binding site under π-π, π-cation, and hydrophobic interactions. The QSAR studies demonstrated that the heteroatoms N and O are important for ligand recognition, which could explain the structural diversity of ligands that reach V1aR.

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Section: Methodsmentioning

confidence: 99%

“…In the field of drug discovery, many efforts have been made to find ligands that act as either agonists or antagonists. Computational tools, such as molecular modeling (including docking and molecular dynamics simulations) and QSAR studies, have been useful in designing new drugs .…”

mentioning

confidence: 99%

Exploring the Ligand Recognition Properties of the Human Vasopressin V1a Receptor Using QSAR and Molecular Modeling Studies

Contreras-Romo¹,

Martínez‐Archundia

Deeb

et al. 2013

Chem Biol Drug Des

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“…These methods have been applied for studying TIs. Several quantitative structure-activity relationship (QSAR) models were reported using approaches such as classic QSAR [ 14 ], CoMFA/CoMSIA [ 15 , 16 ], topological descriptors [ 17 ], and artificial neural networks [ 18 ]. Other reports used docking and molecular dynamics (MD) simulations to study structural features of several TIs identified wit a high activity [ 19 – 21 ].…”

Section: Introductionmentioning

confidence: 99%

Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA

2015

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Non-peptidic thrombin inhibitors (TIs; 177 compounds) with diverse groups at motifs P1 (such as oxyguanidine, amidinohydrazone, amidine, amidinopiperidine), P2 (such as cyanofluorophenylacetamide, 2-(2-chloro-6-fluorophenyl)acetamide), and P3 (such as phenylethyl, arylsulfonate groups) were studied using molecular modeling to analyze their interactions with S1, S2, and S3 subsites of the thrombin binding site. Firstly, a protocol combining docking and three dimensional quantitative structure–activity relationship was performed. We described the orientations and preferred active conformations of the studied inhibitors, and derived a predictive CoMSIA model including steric, donor hydrogen bond, and acceptor hydrogen bond fields. Secondly, the dynamic behaviors of some selected TIs (compounds 26, 133, 147, 149, 162, and 177 in this manuscript) that contain different molecular features and different activities were analyzed by creating the solvated models and using molecular dynamics (MD) simulations. We used the conformational structures derived from MD to accomplish binding free energetic calculations using MM-GBSA. With this analysis, we theorized about the effect of van der Waals contacts, electrostatic interactions and solvation in the potency of TIs. In general, the contents reported in this article help to understand the physical and chemical characteristics of thrombin-inhibitor complexes.

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“…Training models were built using the MLR subroutine of the spss for Windows v13.0 under the stepwise procedure. MLR analysis with stepwise selection and elimination of variables was used to model the relationship of the binding affinity pK i with each set of descriptors by applying the individual approach previously described …”

Section: Methodsmentioning

confidence: 99%

Ligand recognition properties of the vasopressin V2 receptor studied under QSAR and molecular modeling strategies

et al. 2017

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The design of new drugs that target vasopressin 2 receptor (V2R) is of vital importance to develop new therapeutic alternatives to treat diseases such as heart failure, polycystic kidney disease. To get structural insights related to V2R-ligand recognition, we have used a combined approach of docking, molecular dynamics simulations (MD) and quantitative structure-activity relationship (QSAR) to elucidate the detailed interaction of the V2R with 119 of its antagonists. The three-dimensional model of V2R was built by threading methods refining its structure through MD simulations upon which the 119 ligands were subjected to docking studies. The theoretical results show that binding recognition of these ligands on V2R is diverse, but the main pharmacophore (electronic and π-π interactions) is maintained; thus, this information was validated under QSAR results. QSAR studies were performed using MLR analysis followed by ANN analysis to increase the model quality. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals, and they can be useful tools for screening new chemicals for safety assessment.

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Exploring QSARs for inhibitory effect of a set of heterocyclic thrombin inhibitors by multilinear regression refined by artificial neural network and molecular docking simulations

Cited by 11 publications

References 45 publications

Exploring the Ligand Recognition Properties of the Human Vasopressin V1a Receptor Using QSAR and Molecular Modeling Studies

Exploring the Ligand Recognition Properties of the Human Vasopressin V1a Receptor Using QSAR and Molecular Modeling Studies

Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA

Ligand recognition properties of the vasopressin V2 receptor studied under QSAR and molecular modeling strategies

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