Hologram Quantitative Structure-activity Relationship Model Research Articles

HQSAR studies on S-DABO derivatives as HIV-1 reverse transcriptase inhibitors

A hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of 34 S-DABO analogues as HIV-1 reverse transcriptase inhibitors. The influences of fragment distinction, fragment size and the hologram length on the quality of the models were investigated. The best HQSAR model was obtained for the 26 training set compounds showing cross-validated <italic>q</italic>² value of 0.755 and conventional <italic>r</italic>² value of 0.949. The model was then externally validated using a test set of eight compounds and the predicted values were in good agreement with the experimental results (<italic>r</italic>_pred²=0.95). The color code analysis based on the obtained HQSAR model provided useful insights into the chemical and structural features of S-DABO derivatives for their HIV-1 inhibitory potency and should be useful for the design of new potent anti-HIV-1 agents.

Predictive Chemometric Modeling of DPPH Free Radical-Scavenging Activity of Azole Derivatives Using 2D- and 3D-Quantitative Structure–Activity Relationship Tools

The endogenous antioxidants often fail to manage the systemic free radical overload resulting from extensive exposure to environmental pollutants and improper diet. Such free-radical burden over a prolonged period leads to oxidative stress, which in turn, promotes an array of fatal diseases. Five different in silico methodologies have been employed here for a series of azole derivatives, which identify the essential structural attributes of the molecules and quantify the contributions of the prime molecular prerequisites for designing compounds with improved antioxidant activity. The importance of the different constituents is quantitatively analyzed using the descriptor-based quantitative structure-activity relationship and group-based quantitative structure-activity relationship models while the pharmacophore, comparative molecular similarity index analysis and hologram quantitative structure-activity relationship models serve as essential query tools for screening of azole compounds in order to select potent antioxidant molecules.

Discovery of new potential hits of Plasmodium falciparum enoyl-ACP reductase through ligand- and structure-based drug design approaches

We here report the discovery of novel Plasmodium falciparum enoyl-ACP reductase (PfENR) inhibitors as new antimalarial hits through ligand- and structure-based drug design approaches. We performed 2D and 3D QSAR studies on a set of rhodanine analogues using hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. Statistical and satisfactory results were obtained for the best HQSAR (r2 of 0.968 and qLOO2 of 0.751), CoMFA (r2 of 0.955 and qLOO2 of 0.806) and CoMSIA (r2 of 0.965 and qLOO2 of 0.659) models. The information gathered from the QSAR models guided us to design new PfENR inhibitors. Three new hits were predicted with potency in the submicromolar range and presented drug-like properties.

HQSAR Study and Molecular Design of Benzimidazole Derivatives as Corrosion Inhibitors

Hologram quantitative structure-activity relationship (HQSAR) analysis was conducted on a series of benzimidazole compounds to build the HQSAR model between corrosion inhibition properties and molecular structures in acid environment. The optimal HQSAR model was determined by investigating the influence of different fragment distinction and fragment size on the models, and the models stability and predictive ability were evaluated. The results show that the optimal HQSAR model was generated using atoms(A), bonds(B), connectivity(C), hydrogen(H), chirality(Ch), donor and acceptor(DA) as fragment distinction and fragment size of 1-3. The model had a non-cross validated coefficient (r 2 ) value of 0.996, a cross-validated (q 2 ) value of 0.960, and a cross-validated standard error (SE cv ) value of 3.709, which indicates good statistics stability and predictive power. On the basis of the maps derived from the optimal HQSAR model, 38 new benzimidazole derivatives were designed and screened using the optimal HQSAR model, giving potential candidates with high predictive inhibition efficiency. This work provides valuable information for further research and design of more promising corrosion inhibitors in the oil and gas field.

Enhancement of P-gylcoprotein modulators of arylmethylamine-phenyl derivatives: an integrative modeling approach

Multidrug resistance (MDR) plays a crucial role in the failure of cancer treatment with chemotherapeutic agents. In order to overcome the MDR, we have developed both 2D and 3D-QSAR models to enhance the structural requirement for P-gp inhibitors. In this work, hologram quantitative structure–activity relationship (HQSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of arylmethylamine-phenyl derivatives of P-gp inhibitors. The best predictions were obtained for HQSAR model (q 2 = 0.645, r 2 = 0.772), CoMFA model (q 2 = 0.577, r 2 = 0.917), and CoMSIA model (electrostatic, and H-bond donor) (q 2 = 0.610, r 2 = 0.923). Statistical parameters from the generated QSAR models indicated that the data is well fitted and have high predictive ability. HQSAR result showed that donor and acceptor descriptors play an important role in P-gp activity, and methoxy group on the A-ring at R1 position is necessary for increasing activity. The CoMFA and CoMSIA models predicted that steric, electrostatic, and H-bond donor parameters are important for activity toward P-gp. Our theoretical results could be useful to design novel and more potent P-gp derivatives.

Hologram QSAR Models of 4-[(Diethylamino)methyl]-phenol Inhibitors of Acetyl/Butyrylcholinesterase Enzymes as Potential Anti-Alzheimer Agents

Hologram QSAR models were developed for a series of 36 inhibitors (29 training set and seven test set compounds) of acetyl/butyrylcholinesterase (AChE/BChE) enzymes, an attractive molecular target for Alzheimer’s disease (AD) treatment. The HQSAR models (N = 29) exhibited significant cross-validated (AChE, q2 = 0.787; BChE, q2 = 0. 904) and non-cross-validated (AChE, r2 = 0.965; BChE, r2 = 0.952) correlation coefficients. The models were used to predict the inhibitory potencies of the test set compounds, and agreement between the experimental and predicted values was verified, exhibiting a powerful predictive capability. Contribution maps show that structural fragments containing aromatic moieties and long side chains increase potency. Both the HQSAR models and the contribution maps should be useful for the further design of novel, structurally related cholinesterase inhibitors.

Structural and chemical basis for enhanced affinity to a series of mycobacterial thymidine monophosphate kinase inhibitors: fragment-based QSAR and QM/MM docking studies

Tuberculosis (TB) still remains one of the most deadly infectious diseases. Mycobacterium tuberculosis thymidine monophosphate kinase (TMPKmt) has emerged as an attractive molecular target for the design of a novel class of anti-TB agents since blocking it will affect the pathways involved in DNA replication. Aiming at shedding some light on structural and chemical features that are important for the affinity of thymidine derivatives to TMPKmt, we have employed a special fragment-based method to develop robust quantitative structure-activity relationship models for a large and chemically diverse series of thymidine-based analogues. Significant statistical parameters (r2= 0.94, q2= 0.76, r2pred= 0.89) were obtained, indicating the reliability of the hologram QSAR model in predicting the biological activity of untested compounds. The 2D model was then used to predict the potency of an external test set, and the predicted values obtained from the HQSAR model were in good agreement with the experimental results. We have accordingly designed novel TMPKmt inhibitors by utilizing the fragments proposed by HQSAR analysis and predicted with good activity in the developed models. The new designed compounds also presented drug-like characteristics based on Lipinski's rule of 5. The generated molecular recognition patterns gathered from the HQSAR analysis combined with quantum mechanics/molecular mechanics (QM/MM) docking studies, provided important insights into the chemical and structural basis involved in the molecular recognition process of this series of thymidine analogues and should be useful for the design of new potent anti-TB agents.

Insight into the Structural Requirements of Narlaprevir-Type Inhibitors of NS3/NS4A Protease Based on HQSAR and Molecular Field Analyses

In the life cycle of hepatitis C virus (HCV), NS3/NS4A protease has been proved to play a vital role in the replication of the HCV virus. Narlaprevir and its derivatives, the inhibitors of NS3/NS4A, would be potentially developed as important anti-HCV drugs in the future. In this study, quantitative structure-activity relationship (QSAR) analyses for 190 narlaprevir derivatives were conducted using comparative molecular field analysis (CoMFA), comparative molecular indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) techniques. Both of the best CoMFA and HQSAR models showed statistical significance for the training set and good predictive accuracy for the test set, which strongly manifested the robustness of the CoMFA and HQSAR models. The CoMFA contour maps and the HQSAR contribution maps were both presented. Furthermore, based on the essential factors for ligand binding derived from the QSAR models, sixteen new derivatives were designed and some of them showed higher inhibitory activities confirmed by our models and molecular docking studies. General speaking, this study provides useful suggestions for the design of potential anti-HCV drugs.

Quantitative structure-activity relationship of 2-alkyl-4-(biphenylylmethoxy) pyridine derivatives with AT1 receptor antagonistic activity

The quantitative structure-activity relationship (QSAR) of 2-alkyl-4-(biphenylylmethoxy) pyridine derivatives was studied. Three different alignment methods were used to get the models of the comparative molecular field analysis (CoMFA), the comparative molecular similarity indices analysis (CoMSIA), and the hologram quantitative structure-activity relationship (HQSAR). The statistical results from the established models show believable predictivity based on the cross-validated value (q2>0.5) and the non-validated value (r2>0.9). The analysis on contour maps of CoMFA and CoMSIA models suggests that hydrophobic and hydrogen-bond acceptor fields are important factors that affect the AT1 antagonistic activity of 2-alkyl-4-(biphenylylmethoxy) pyridine derivatives besides the steric and electrostatic fields. The structural modification information from different atom contributions in the HQSAR model is in agreement with that in the 3D-QSAR models.

QSAR and Docking Studies on Different Series of Histone Deacetylase Inhibitors (HDACIs)

Quantitative structure-activity relationships (QSAR) of 65 different molecular series histone deacetylase inhibitors (HDACIs) with only one common pyrrole ring were studied by using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR). The leave-one-out cross-validation q2 values of CoMFA, CoMSIA and HQSAR models were 0.623, 0.662 and 0.716, and non-cross-validated r2 values were 0.952, 0.909 and 0.891, respectively. The contour maps and atom contribution map provided helpful visualization information for structural modification of the HDACIs for better activity. A further docking study was carried out by using Surflex-dock method. Sixty-five compounds were docked into histone deacetylase (PDB ID 1T64). A reasonable docking model between inhibitors and the protein was obtained. The interaction between ligands and the receptor can reveal the binding mode of the HDAC inhibitors and HDAC enzyme. According to QSAR and docking results, eight new molecules were proposed, and their activities were predicted by using the established QSAR model and docking method. The results indicated that the proposed potential molecules have a potent activity. Keywords: CoMFA, CoMSIA, HQSAR, HDACIs, Surflex-dock

In silico development, validation and comparison of predictive QSAR models for lipid peroxidation inhibitory activity of cinnamic acid and caffeic acid derivatives using multiple chemometric and cheminformatics tools

The design and development of antioxidant molecules have lately gained a great deal of focus which is attributed to their immense biomedicinal importance in combating the free radical associated health hazards. In a situation to replenish the endogenous antioxidant loss, synthetic molecules with potent antioxidant activity is demanded. The present work thus aims at in silico modeling of antioxidant molecules that may facilitate in searching and designing of new chemical entities with enhanced activity profile. A series of cinnamic acid and caffeic acid derivatives having the ability to inhibit lipid peroxidation have been modeled in the present work. Three different types of models were developed using different chemometric and cheminformatics tools to identify the essential structural attributes: (a) descriptor based QSAR models, (b) 3D pharmacophore models and (c) HQSAR (hologram QSAR) models. For the conventional QSAR modeling, descriptors belonging to different categories [quantum chemical descriptors (Mulliken charges of the common atoms of the molecules), thermodynamic descriptors, electronic descriptors, structural descriptors and spatial descriptors] were calculated for the development of statistically significant as well as well interpretable quantitative structure-activity relationship (QSAR) models. Two different chemometric tools [genetic function approximation (GFA) and genetic partial least squares (G/PLS)] were employed for the development of the QSAR models. The 3D pharmacophore model focused on the essential pharmacophoric features while the HQSAR model implicated the prime structural fragments that were necessitated for the optimal anti-lipid peroxidative activity of the molecules. All the models were validated based on internal, external and overall validation statistics. Randomization was performed in order to ensure the absence of chance correlation in the developed models. Among all models, the descriptor-based model developed using the GFA-spline technique yielded the most satisfactory results. The results obtained from all the models corroborate well with each other and chiefly signify the importance of the ketonic oxygen of the amide/ acid fragment and the ethereal oxygen substituted on the parent phenyl ring of the molecules under study. Thus the models can efficiently be utilized for extensive screening of large datasets and their subsequent activity prediction.

Quantitative structure–activity relationship and design of polysubstituted quinoline derivatives as inhibitors of phosphodiesterase 4

2D quantitative structure–activity relationships (2D QSAR) and hologram quantitative structure–activity relationship (HQSAR) studies were performed on a series of polysubstituted quinoline derivatives as inhibitors of phosphodiesterase 4 (PDE4). The dataset was divided into training set and test set by K-means clustering. 2D QSAR study was carried out using stepwise linear regression analysis, replacement method and enhanced replacement method. Statistically significant equations with high correlation coefficient (R 2 = 0.817) and low standard deviation (SD = 0.272) were obtained. The robustness of the models was confirmed with the help of leave one out cross validation (R cv 2 = 0.740), Y scrambling (R 2 = 0.374), and by predicting the activities of test molecules (R pred 2 = 0.627). A good correlation of topology, steric and polar features of polysubstituted quinoline derivatives with the PDE4 inhibitory activity was achieved. HQSAR calculations were carried out using various combinations of fragment size, hologram length and fragment type. The best HQSAR model was obtained with an R 2 value of 0.952 and $$ R_{\text{cv}}^{2} $$ value of 0.783. The test-set of molecules was predicted by the HQSAR model. The results of 2D QSAR and HQSAR studies were used to design new molecules and to predict their activity using the developed models.

Comparative quantitative structure–activity relationship (QSAR) study on acute toxicity of triazole fungicides to zebrafish

We employed two-dimensional quantitative structure–activity relationship (2D-QSAR) and hologram QSAR (HQSAR) methods to quantitatively investigate the mechanism and active site of toxicity for Danio rerio exposed to triazole fungicides. Our results showed that 2D-QSAR models constructed using the energy of the lowest unoccupied molecular orbit, the net C atom charges, the octanol–water partition coefficient and the molecular shape factor had higher predictive abilities. HQSAR models containing the fragment distinctions atoms (As), bonds (Bs), chirality (Ch) and donors and acceptors (D&A) had higher reliability. It was found that 2D-QSAR results explaining the toxicity mechanism were consistent with HQSAR. In summary, the hydrophobicity and shape/size of the molecule were the important factors influencing the toxic effect of these chemicals against D. rerio. In addition, electron exchange may occur between these fungicides and the target. The study provided a method to evaluate the environmental risk of chemicals with a similar structure, based on the QSAR models obtained.

Integration of Ligand- and Target-Based Virtual Screening for the Discovery of Cruzain Inhibitors.

A myriad of methods are available for virtual screening of small organic compound databases. In this study we have successfully applied a quantitative model of consensus measurements, using a combination of 3D similarity searches (ROCS and EON), Hologram Quantitative Structure Activity Relationships (HQSAR) and docking (FRED, FlexX, Glide and AutoDock Vina), to retrieve cruzain inhibitors from collected databases. All methods were assessed individually and then combined in a Ligand-Based Virtual Screening (LBVS) and Target-Based Virtual Screening (TBVS) consensus scoring, using Receiving Operating Characteristic (ROC) curves to evaluate their performance. Three consensus strategies were used: scaled-rank-by-number, rank-by-rank and rank-by-vote, with the most thriving the scaled-rank-by-number strategy, considering that the stiff ROC curve appeared to be satisfactory in every way to indicate a higher enrichment power at early retrieval of active compounds from the database. The ligand-based method provided access to a robust and predictive HQSAR model that was developed to show superior discrimination between active and inactive compounds, which was also better than ROCS and EON procedures. Overall, the integration of fast computational techniques based on ligand and target structures resulted in a more efficient retrieval of cruzain inhibitors with desired pharmacological profiles that may be useful to advance the discovery of new trypanocidal agents.

QSAR analysis on PfPK7 inhibitors using HQSAR, CoMFA, and CoMSIA

Plasmodium falciparum protein kinase 7 (PfPK7) is an important drug target for the development of anti-malarial treatment. In this study, hologram quantitative structure–activity relationship (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of imidazopyridazine derivatives of PfPK7 inhibitors. The best HQSAR model was obtained using atoms, connection, donor, and acceptor as fragment distinction parameter with fragment size (4–7) using a hologram length of 353 and 6 components (q2 = 0.770, r2 = 0.964). The receptor-guided alignment has produced better statistical results for both CoMFA (q2 = 0.590, r2 = 0.986) and CoMSIA (q2 = 0.735, r2 = 0.988). The predictive ability of the developed models was further validated by a test set of eight compounds. HQSAR contribution map identified the presence of phenyl ring and cyclohexane moiety makes positive contribution for activity. Furthermore, CoMFA and CoMSIA contour maps suggested that additional bulky groups in cyclohexane moiety would increase the biological activity of PfPK7 inhibitors. Finally, these QSAR models were used to design new virtual molecules for imidazopyridazine derivatives and the results obtained from this study could be useful for further investigations.

In Silico Quantitative Structure-Activity Relationship Studies on P-gp Modulators of Tetrahydroisoquinoline-Ethyl-Phenylamine Series

BackgroundMultidrug resistance (MDR) is a major obstacle in cancer chemotherapy. The drug efflux by a transport protein is the main reason for MDR. In humans, MDR mainly occurs when the ATP-binding cassette (ABC) family of proteins is overexpressed simultaneously. P-glycoprotein (P-gp) is most commonly associated with human MDR; it utilizes energy from adenosine triphosphate (ATP) to transport a number of substrates out of cells against concentration gradients. By the active transport of substrates against concentration gradients, intracellular concentrations of substrates are decreased. This leads to the cause of failure in cancer chemotherapy.ResultsHerein, we report Topomer CoMFA (Comparative Molecular Field Analysis) and HQSAR (Hologram Quantitative Structure Activity Relationship) models for third generation MDR modulators. The Topomer CoMFA model showed good correlation between the actual and predicted values for training set molecules. The developed model showed cross validated correlation coefficient (q2) = 0.536 and non-cross validated correlation coefficient (r2) = 0.975 with eight components. The best HQSAR model (q2 = 0.777, r2 = 0.956) with 5-8 atom counts was used to predict the activity of test set compounds. Both models were validated using test set compounds, and gave a good predictive values of 0.604 and 0.730.ConclusionsThe contour map near R1 indicates that substitution of a bulkier and polar group to the ortho position of the benzene ring enhances the inhibitory effect. This explains why compounds with a nitro group have good inhibitory potency. Molecular fragment analyses shed light on some essential structural and topological features of third generation MDR modulators. Fragments analysis showed that the presence of tertiary nitrogen, a central phenyl ring and an aromatic dimethoxy group contributed to the inhibitory effect. Based on contour map information and fragment information, five new molecules with variable R1 substituents were designed. The activity of these designed molecules was predicted by the Topomer CoMFA and HQSAR models. The novel compounds showed higher potency than existing compounds.

Two-dimensional quantitative structure-activity relationship studies on bioactive ligands of peroxisome proliferator-activated receptor δ

Peroxisome proliferator-activated receptors (PPARs) form a subclass of the nuclear receptor superfamily and are attractive drug targets for the development of novel therapeutic agents to treat several metabolic disorders, such as dyslipidemia and type 2 diabetes mellitus. In this work, hologram quantitative structure-activity relationship (HQSAR) studies were carried out on a series of potent PPARδ ligands. Significant correlation coefficients (r² = 0.947 and q² = 0.791) were obtained, indicating the reliability of the 2D QSAR model in predicting the biological activity of untested compounds. The 2D model was then used to predict the potency of an external test set, and the predicted values obtained from the HQSAR model were in good agreement with the experimental results. The final QSAR model, along with the information obtained from 2D contribution maps, should be useful for the design of novel PPARδ ligands having improved potency.

Studies on two types of PTP1B inhibitors for the treatment of type 2 diabetes: Hologram QSAR for OBA and BBB analogues

Hologram quantitative structure–activity relationships (HQSAR) analysis were conducted on two series of PTP1B inhibitors, 39 2-(oxalylamino) benzoic acid (OBA) analogues and 60 benzofuran and benzothiophene biphenyls (BBB) analogues. The optimal HQSAR model of the OBA analogue has q2=0.592 and r2=0.940, while the optimal HQSAR model for the BBB analogues shows q2=0.667 and r2=0.863. Two models were employed to predict the biological activities of two test sets. For OBA analogues, the optimal model was validated by an external test set of six compounds with satisfactory predictive r2 value of 0.786. For BBB analogues, the optimal model shows satisfactory predictive r2 value of 0.866 for an external test set of 10 compounds. The contribution maps derived from the optimal HQSAR models are consistent with the biological activities of the studied compounds. Two virtual combinatorial libraries were designed and screened by the optimal HQSAR models and potential candidates with high predictive biological activities were discovered. This work may provide valuable information for future design of more promising inhibitors for PTP1B.

Studies of cannabinoid-1 receptor antagonists for the treatment of obesity: Hologram QSAR model for biarylpyrazolyl oxadiazole ligands

Hologram QSAR studies were conducted on a series of 60 training set of cannabinoid-1 receptor (CB 1) antagonists. Significant cross-validated correlation coefficients ( q 2 = 0.763) and noncross-validated correlation coefficients ( r 2 = 0.897) were obtained. The model was then employed to predict the biological activities of 15 test set compounds, and a good agreement between the experimental and predicted values was verified exhibiting a powerful predictable capability of this model ( q pred 2 = 0.868 ) . Contribution map shows that 1,2,4-trizole and cyclopropane moieties make big contributions for the activities. Both the HQSAR model and analysis from the contribution map should be useful for the further design of novel structurally related CB 1 antagonists.

2D- and 3D-QSAR studies on 54 anti-tumor Rubiaceae-type cyclopeptides

RA-VII, a bicyclic hexapeptide isolated from the roots of Rubia cordifolia, Rubia akane belongs to Rubiaceae-type cyclopeptides (RAs) and has attracted much attention for its potent anti-tumor activity and its bicyclic structure incorporating the isodityrosine moiety. In this work, hologram quantitative structure–activity relationship (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods were employed to develop 2D- and 3D-QSAR models for 54 anti-tumor RAs. The LOO cross-validated q 2 values of HQSAR, CoMFA and CoMSIA models are 0.701, 0.510 and 0.613, respectively. The predictive ability of these models was validated by the test set including 7 RAs, and the predicted IC 50 values were in good agreement with the experimental IC 50 values. HQSAR result showed that chirality descriptor plays an important role in anti-tumor activity of RAs and OMe at R 1 and R 2 is necessary for increasing their activity. CoMFA and CoMSIA results demonstrated that small bulky and electropositive side chains at R 3 position and hydrophobic groups at R 7 and R 8 positions will increase their activity, and intra-molecular hydrogen bonds between residues 1 and 4 are necessary to maintain the pharmacophoric conformation of RAs. These results may be helpful in designing novel and potential anti-tumor RAs.