Hologram QSAR Research Articles

Development of multiple QSAR models for consensus predictions and unified mechanistic interpretations of the free-radical scavenging activities of chromone derivatives

Antioxidants are important defenders of the human body against nocive free radicals, which are the causative agents of most life-threatening diseases. The immense biomedicinal utility of antioxidants necessitates the development and design of new synthetic antioxidant molecules. The present report deals with the modeling of a series of chromone derivatives, which was done to provide detailed insight into the main structural fragments that impart antioxidant activity to these molecules. Four different quantitative structure-property relationship (QSAR) techniques, namely 3D pharmacophore mapping, comparative molecular similarity indices analysis (CoMSIA 3D-QSAR), hologram QSAR (HQSAR), and group-based QSAR (G-QSAR) techniques, were employed to obtain statistically significant models with encouraging external predictive potentials. Moreover, the visual contribution maps obtained for the different models signify the importance of different structural features in specific regions of the chromone nucleus. Additionally, the G-QSAR models determine the composite influence of pairs of substituent fragments on the overall antioxidant activity profiles of the molecules. Multiple models with different strategies for assessing structure-activity relationships were applied to reach a unified conclusion regarding the antioxidant mechanism and to provide consensus predictions, which are more reliable than values derived from a single model. The structural information obtained from the various QSAR models developed in the present work can thus be effectively utilized to design and predict the activities of new molecules belonging to the class of chromone derivatives.

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.

Immunoassay for Phenylurea Herbicides: Application of Molecular Modeling and Quantitative Structure–Activity Relationship Analysis on an Antigen–Antibody Interaction Study

An indirect competitive enzyme-linked immunosorbent assay (icELISA) for 12 phenylurea herbicides (PUHs) was established with the half-maximum inhibition concentration (IC(50)) of 1.7-920.7 μg L(-1). A method of computer-aided molecular modeling was established in quantitative structure-activity relationship (QSAR) studies to obtain a deeper insight into the PUHs' antibody interactions on how and which molecular properties of the analytes quantitatively affect the antibody recognition. A two-dimensional (2D)-QSAR model based on the Hansch equation and a hologram QSAR (HQSAR) model were constructed, and both showed highly predictive abilities with cross-validation q(2) values of 0.820 and 0.752, respectively. It was revealed that the most important impact factor of the antibody recognition was the PUHs' hydrophobicity (log P), which provided a quadratic correlation to the antibody recognition. Hapten-carrier linking groups were less exposed to antibodies during immunization; thus, groups of the analytes in the same position were generally considered to be less contributive to antibody recognition during immunoassay. But the results of substructure-level analysis showed that these groups played an important role in the antigen-antibody interaction. In addition, the frontier-orbital energy parameter E(LUMO) was also demonstrated as a related determinant for this reaction. In short, the result demonstrated that the hydrophobicity and the lowest unoccupied molecular orbital energy (E(LUMO)) of PUH molecules were mainly responsible for antibody recognition.

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.

Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods.

Research Article: Insights into the Molecular Requirements for the Anti‐obesity Activity of a Series of CB1 Ligands

Two‐dimensional and 3D quantitative structure–activity relationships studies were performed on a series of diarylpyridines that acts as cannabinoid receptor ligands by means of hologram quantitative structure–activity relationships and comparative molecular field analysis methods. The quantitative structure–activity relationships models were built using a data set of 52 CB1 ligands that can be used as anti‐obesity agents. Significant correlation coefficients (hologram quantitative structure–activity relationships: r 2 = 0.91, q 2 = 0.78; comparative molecular field analysis: r 2 = 0.98, q 2 = 0.77) were obtained, indicating the potential of these 2D and 3D models for untested compounds. The models were then used to predict the potency of an external test set, and the predicted (calculated) values are in good agreement with the experimental results. The final quantitative structure–activity relationships models, along with the information obtained from 2D contribution maps and 3D contour maps, obtained in this study are useful tools for the design of novel CB1 ligands with improved anti‐obesity potency.

Molecular Docking and QSAR Studies on Substituted Acyl(thio)urea and Thiadiazolo [2,3‐α] Pyrimidine Derivatives as Potent Inhibitors of Influenza Virus Neuraminidase

Surflex-Dock was employed to dock 36 thiourea and thiadiazolo [2,3-alpha] pyrimidine derivatives into neuraminidase 1a4g. Molecular docking results showed that hydrogen bonding, electrostatic, and hydrophobic features were important factors affecting inhibitory activities of these neuraminidase inhibitors. Moreover, there was a significant correlation between the predicted binding affinity (total scores) and experimental pIC50 values with correlation coefficient r=0.846 and p<0.0001. Hologram quantitative structure-activity relationship, comparative molecular field analysis, and comparative molecular similarity indices analysis were used to develop quantitative structure-activity relationship models. Squared multiple correlation coefficients (r2) of hologram quantitative structure-activity relationship, comparative molecular field analysis, and comparative molecular similarity indices analysis models were 0.899, 0.878, and 0.865, respectively. Squared cross-validated correlation coefficient (q2) of hologram quantitative structure-activity relationship, comparative molecular field analysis, and comparative molecular similarity indices analysis models was in turn 0.628, 0.656, and 0.509. In addition, squared multiple correlation coefficients for test set (r2test) of hologram quantitative structure-activity relationship, comparative molecular field analysis, and comparative molecular similarity indices analysis models were 0.558, 0.667, and 0.566, respectively. The most active sample ID 2 was taken as a template molecule to design new molecules. Based on the comparative molecular field analysis model, new compounds were designed by LeapFrog. Seven new compounds with improved binding energy and predicted activities were finally obtained.

QSAR Studies on HSV-1 Inhibitors by CoMFA, CoMSIA and HQSAR Approaches

HSV infection has high incidence rate and high prevalence. HSV-1 may cause cold sores, fever blisters, and encephalitis. Moreover, HSV-1 has the ability to cause the latent infection of neurons. Therefore, the QSAR studies of HSV-1 inhibitors and drug design based on QSAR have great importance. This paper focused on the pyrazolo[1,5- a]pyridines, which are not acyclic nucleoside analog. The reported pyrazolopyridines showed potent and selective inhibition activity. In order to find novel compounds with higher activity and to develop better predictive models, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram quantitative structure activity relationship (HQSAR) methods were performed on a series of HSV-1 inhibitors. The models built by CoMFA, CoMSIA, and HQSAR were found to be suitable for the compounds investigated with good predictive power (CoMFA q2=0.669, R2=0.953; CoMSIA q2=0.693, R2=0.919; HQSAR q2=0.619, R2=0.921). 3D contour maps obtained from PLS models of CoMFA and CoMSIA, and the atom contribution map from PLS model of HQSAR indicated that small steric volumes and electron-withdrawing groups in the R2, R3 regions would be useful to improve the activity. Results showed that the established models of the present work would be helpful in studying the relationship between the bioactivity and the molecular structures and discovering more potent HSV-1inhibitors.

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.

Hologram QSAR Studies of N,N-Dialkyl-2-phenylindol-3-ylglyoxylamides Derivatives as Selective Peripheral Benzodiazepine Receptor Ligands

Hologram QSAR Studies of N,N-Dialkyl-2-phenylindol-3-ylglyoxylamides Derivatives as Selective Peripheral Benzodiazepine Receptor Ligands

Synthesis, Biological Activity, and Hologram Quantitative Structure−Activity Relationships of Novel Allatostatin Analogues

Cockroach-type allatostatins (FGLamides) (ASTs) can inhibit the production of juvenile hormone in vitro, and they therefore are regarded as possible insect growth regulator (IGR) candidates for pest control. However, several shortcomings, such as the absence of in vivo effects, rapid degradation, and high production costs, preclude their practical use in pest management. To discover new IGRs, 25 novel analogues of pentapeptide (Y/FXFGLa) were designed and synthesized with different aromatic acids, fatty acids, and dicarboxylic acids as the Y/FX region replacements on the basis of previous results. Their bioactivities in vitro were determined, and the results showed that eight analogues (K14, K15, K17, K18, K19, K23, K24, and K25) were more active than the lead, core region pentapeptide. The IC(50) values of K15 and K24 (IC(50) = 1.79 and 5.32 nM, respectively) were even lower than that of the natural AST, Dippu-AST 1(IC(50) = 8 nM), which indicated both analogues have better activity than Dippu-AST 1; particularly, K15 has better activity than most natural Dippu-ASTs. A predictable and statistically meaningful hologram quantitative structure-activity relationship (HQSAR) model of 32 AST analogues (28 as training sets and 4 as test sets) was obtained. The final model suggested that a potent AST analogue should contain an aromatic group, a linker of appropriate length, and the FGLa portion. These results will be useful in the design of new AST analogues that are structurally related to the training set compounds.

Hologram and Receptor-Guided 3D QSAR Analysis of Anilinobipyridine JNK3 Inhibitors

Hologram and three dimensional quantitative structure activity relationship (3D QSAR) studies for a series of anilinobipyridine JNK3 inhibitors were performed using various alignment-based comparative molecular field analysis (COMFA) and comparative molecular similarity indices analysis (CoMSIA). The in vitro JNK3 inhibitory activity exhibited a strong correlation with steric and electrostatic factors of the molecules. Using four different types of alignments, the best model was selected based on the statistical significance of CoMFA (<TEX>$q_2\;=\;0.728,\;r_2\;=\;0.865$</TEX>), CoMSIA (<TEX>$q_2\;=\;0.706,\;r_2\;=\;0.960$</TEX>) and Hologram QSAR (HQSAR: <TEX>$q_2\;=\;0.838,\;r_2\;=\;0.935$</TEX>). The graphical analysis of produced CoMFA and CoMSIA contour maps in the active site indicated that steric and electrostatic interactions with key residues are crucial for potency and selectivity of JNK3 inhibitors. The HQSAR analysis showed a similar qualitative conclusion. We believe these findings could be utilized for further development of more potent and selective JNK3 inhibitors.

In silico QSAR studies of anilinoquinolines as EGFR inhibitors

Members of the epidermal growth factor receptor (EGFR) family of proteins are frequently overactive in solid tumors. A relatively new therapeutic approach to inhibit the kinase activity is the use of ATP-competitive small molecules. In silico techniques were employed to identify the key interactions between inhibitors and their protein receptors. A series of EGFR inhibitory anilinoquinolines was studied within the framework of hologram quantitative structure activity relationship (HQSAR), density functional theory (DFT)-based QSAR, and three-dimensional (3D) QSAR (CoMFA/CoMSIA). The HQSAR analysis implied that substitutions at certain sites on the inhibitors play an important role in EGFR inhibition. DFT-based QSAR results suggested that steric and electronic interactions contributed significantly to the activity. Ligand-based 3D-QSAR and receptor-guided 3D-QSAR analyses such as CoMFA and CoMSIA techniques were carried out, and the results corroborated the previous two approaches. The 3D QSAR models indicated that steric and hydrophobic interactions are dominant, and that substitution patterns are an important factor in determining activity. Molecular docking was helpful in identifying a bioactive conformer as well as a plausible binding mode. The docked geometry-based CoMFA model with steric and electrostatic fields effect gave q(2) = 0.66, r(2) = 0.94 with r(2) (predictive) = 0.72. Similarly, CoMSIA with hydrophobic field gave q(2) = 0.59, r(2) = 0.85 with r(2) (predictive) = 0.63. Bulky groups around site 3 of ring "C", and hydrophilic and bulky groups at position 6 of ring "A" are desirable, with a hydrophobic and electron-donating group at site 7 of ring "A" being helpful. Accordingly, potential EGFR inhibitors may be designed by modification of known inhibitors.

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.

Fragment-based QSAR: perspectives in drug design

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure-activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.

2D QSAR studies on a series of bifonazole derivatives with antifungal activity

Candida albicans (CA) has been identified as the major opportunistic pathogen in immunosuppressed patients. Most of currently available drugs are either highly toxic or becoming ineffective against resistant strains. An approach to overcome this burden relies on azole derivatives with increased potency and selectivity. Aiming at shedding some light on structural and chemical features that are important for the antifungal activity of azole derivatives, classical 2D QSAR and hologram QSAR (HQSAR) studies were performed for a diverse set of 52 bifonazole derivatives with antifungal activity. Topological descriptors, employed in Classical QSAR studies, resulted in models with low correlation (r² = 0.38, q² = 0.27) and lack of predictive power (r²pred = -0.6). On the other hand molecular holograms afforded HQSAR models with good correlation coefficients (r² = 0.92, q² = 0.65) and good predictive ability (r²pred = 0.79).