Quantitative structure activity relationship studies of aryl heterocycle-based thrombin inhibitors

QSAR Study on Antioxidant Tripeptides and the Antioxidant Activity of the Designed Tripeptides in Free Radical Systems

In this study, quantitative structure-activity relationship (QSAR) models were determined based on 91 antioxidant tripeptides. We firstly adopted the stepwise regression (SWR) method for selecting key variables without autocorrelation and then utilized multiple linear regression (MLR), support vector machine (SVM), random forest (RF), and partial least square regression (PLS) to develop predictive QSAR models based on the screened variables. The results demonstrated that all the established models have good reliability (R²_train > 0.86, Q²_train > 0.70) and relatively good predictability (R²_test > 0.88). The contribution of amino acid residues was calculated from the stepwise regression combined with multiple linear regression (SWR-MLR) method model that shows Trp, Tyr, or Cys at C-terminus is favorable for antioxidant activity of tripeptides. Nineteen antioxidant tripeptides were designed based on SWR-MLR models, and the antioxidant activity of these tripeptides were evaluated using three antioxidant assays in free radical systems (1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging capacity, trolox equivalent antioxidant capacity assay, and the ferric reducing antioxidant power assay). The experimental antioxidant activities of these tripeptides were higher than the calculated/predicted activity values of the QSAR models. The QSAR models established can be used to identify and screen novel antioxidant tripeptides with high activity.

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

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.

Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamics simulations

Coagulation factor Xa (Factor Xa, FXa) is a particularly promising target for novel anticoagulant therapy. The first oral factor Xa inhibitor has been approved in the EU and Canada in 2008. In this work, 38 [6,6,5] Tricyclic Fused Oxazolidinones were studied using a combination of molecular modeling techniques including three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, molecular dynamics and Topomer CoMFA (comparative molecular field analysis) were used to build 3D-QSAR models. The results show that the best CoMFA model has q(2)=0.511 and r(2)=0.984, the best CoMSIA (comparative molecular similarity indices analysis) model has q(2)=0.700 and r(2)=0.993 and the Topomer CoMFA analysis has q(2)=0.377 and r(2)=0.886. The results indicated the steric, hydrophobic, H-acceptor and electrostatic fields play key roles in models. Molecular docking and molecular dynamics explored the binding relationship of the ligand and the receptor protein.

Development of predictive quantitative structure-activity relationship models of epipodophyllotoxin derivatives

Epipodophyllotoxins are the most important anticancer drugs used in chemotherapy for various types of cancers. To further, improve their clinical efficacy a large number of epipodophyllotoxin derivatives have been synthesized and tested over the years. In this study, a quantitative structure-activity relationship (QSAR) model has been developed between percentage of cellular protein-DNA complex formation and structural properties by considering a data set of 130 epipodophyllotoxin analogues. A systematic stepwise searching approach of zero tests, missing value test, simple correlation test, multicollinearity test, and genetic algorithm method of variable selection was used to generate the model. A statistically significant model (r((train))(2) = 0.721; q(cv)(2) = 0.678) was obtained with descriptors such as solvent-accessible surface area, heat of formation, Balaban index, number of atom classes, and sum of E-state index of atoms. The robustness of the QSAR models was characterized by the values of the internal leave-one-out cross-validated regression coefficient (q(cv)(2)) for the training set and r((test))(2) for the test set. The root mean square error between the experimental and predicted percentage of cellular protein-DNA complex formation (PCPDCF) was 0.194 and r((test))(2) = 0.689, revealing good predictability of the QSAR model.

Molecular Modeling and Structure-Activity Relationship of Podophyllotoxin and Its Congeners

A quantitative structure-activity relationship (QSAR) model has been developed between cytotoxic activity and structural properties by considering a data set of 119 podophyllotoxin analogs based on 2D and 3D structural descriptors. A systematic stepwise searching approach of zero tests, a missing value test, a simple correlation test, a multicollinearity test, and a genetic algorithm method of variable selection was used to generate the model. A statistically significant model ( r train2 = 0.906; q cv2 = 0.893) was obtained with the molecular descriptors. The robustness of the QSAR model was characterized by the values of the internal leave-one-out cross-validated regression coefficient ( q cv2) for the training set and r test2 for the test set. The overall root mean square error (RMSE) between the experimental and predicted pIC50 value was 0.265 and r test2 = 0.824, revealing good predictability of the QSAR model. For an external data set of 16 podophyllotoxin analogs, the QSAR model was able to predict the tubulin polymerization inhibition and mechanistically cytotoxic activity with an RMSE value of 0.295 in comparison to experimental values. The QSAR model developed in this study shall aid further design of novel potent podophyllotoxin derivatives.

Docking and 3D-QSAR studies of acetohydroxy acid synthase inhibitor sulfonylurea derivatives

Docking and three dimensional quantitative-structure activity relationship (3D-QSAR) studies were performed on acetohydroxy acid synthase (AHAS) inhibitor sulfonylurea analogues with potential herbicidal activity. The 3D-QSAR studies were carried out using shape, spatial and electronic descriptors along with a few structural parameters. Genetic function approximation (GFA) was used as the chemometric tool for this analysis. The whole data set (n = 45) was divided into a training set (75% of the data set) and a test set (remaining 25%) on the basis of the K-means clustering technique on a standardised topological, physicochemical and structural descriptor matrix. Models developed from the training set were used to predict the activity of the test set compounds. All models were validated internally, externally and using the Y-randomisation technique. Docking studies suggested that the molecules bind within a pocket of the enzyme formed by some important amino acid residues (Met351, Asp375, Arg377, Gly509, Met570 and Val571). In QSAR studies, molecular shape analysis showed that bulky substitution at the R(1) position may enhance AHAS inhibitory activity. Charged surface area descriptors suggested that negative charge distributed over a large surface area may enhance this activity. The hydrogen bond acceptor parameter supported the charged surface area descriptors and suggested that, for better activity, the number of electronegative atoms present in the molecule should be high. The spatial descriptors show that, for better activity, the molecules should possess a bulky substituent and a small substitution at the R(2) and R(3) positions, respectively.

Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models

Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

Quantitative structure-property relationship studies on amino acid conjugates of jasmonic acid as defense signaling molecules

Jasmonates and related compounds, including amino acid conjugates of jasmonic acid, have regulatory functions in the signaling pathway for plant developmental processes and responses to the complex equilibrium of biotic and abiotic stress. But the molecular details of the signaling mechanism are still poorly understood. Statistically significant quantitative structure-property relationship models (r(2) > 0.990) constructed by genetic function approximation and molecular field analysis were generated for the purpose of deriving structural requirements for lipophilicity of amino acid conjugates of jasmonic acid. The best models derived in the present study provide some valuable academic information in terms of the 2/3D-descriptors influencing the lipophilicity, which may contribute to further understanding the mechanism of exogenous application of jasmonates in their signaling pathway and designing novel analogs of jasmonic acid as ecological pesticides.

Quantitative structure-activity relationship studies on 1-aryl-tetrahydroisoquinoline analogs as active anti-HIV agents

Predictive quantitative structure-activity relationship analysis was developed for a diverse series of recently synthesized 1-aryl-tetrahydroisoquinoline analogs with anti-HIV activities in this study. The conventional 2D-QSAR models were developed by genetic function approximation (GFA) and stepwise multiple linear regression (MLR) with acceptable explanation of 94.9% and 95.5% and good predicted power of 91.7% and 91.7%, respectively. The results of the 2D-QSAR models were further compared with 3D-QSAR model generated by molecular field analysis (MFA), investigating the substitutional requirements for the favorable receptor-drug interaction and quantitatively indicating the important regions of molecules for their activities. The results obtained by combining these methodologies give insights into the key features for designing more potent analogs against HIV.

Synthesis and anti-platelet activity of novel arylsulfonate–acylhydrazone derivatives, designed as antithrombotic candidates

In this work, we describe a new class of promising anti-platelet drug candidates with significant antithrombotic activity in vivo. This new series of compounds was structurally planned by modification of known thrombin inhibitors based on the use of acylhydrazone subunit, as a nonpeptide scaffold, and variations at P1 moiety. Three different families of arylsulfonate-acylhydrazone derivatives were designed. The bioassays indicated the first class of derivatives represented by 4f (LASSBio-693) and 4j (LASSBio-743), which were active in inhibiting the platelet aggregation induced by thrombin. The second class represented by compounds 4e (LASSBio-774) and 4h (LASSBio-480) that selectively inhibit the platelet aggregation involving TXA(2) formation. Finally, the third class of derivatives was identified acting as a novel symbiotic agent able to inhibit the platelet aggregation induced by collagen or AA and by thrombin, represented by compounds 4b (LASSBio-694) and 4g (LASSBio-770).