Hologram Quantitative Structure-activity Relationship Model Research Articles

Anti-Mycobacterial Evaluation of 7-Chloro-4-Aminoquinolines and Hologram Quantitative Structure-Activity Relationship (HQSAR) Modeling of Amino-Imino Tautomers.

In an ongoing research program for the development of new anti-tuberculosis drugs, we synthesized three series (A, B, and C) of 7-chloro-4-aminoquinolines, which were evaluated in vitro against Mycobacterium tuberculosis (MTB). Now, we report the anti-MTB and cytotoxicity evaluations of a new series, D (D01–D21). Considering the active compounds of series A (A01–A13), B (B01–B13), C (C01–C07), and D (D01–D09), we compose a data set of 42 compounds and carried out hologram quantitative structure–activity relationship (HQSAR) analysis. The amino–imino tautomerism of the 4-aminoquinoline moiety was considered using both amino (I) and imino (II) forms as independent datasets. The best HQSAR model from each dataset was internally validated and both models showed significant statistical indexes. Tautomer I model: leave-one-out (LOO) cross-validated correlation coefficient (q2) = 0.80, squared correlation coefficient (r2) = 0.97, standard error (SE) = 0.12, cross-validated standard error (SEcv) = 0.32. Tautomer II model: q2 = 0.77, r2 = 0.98, SE = 0.10, SEcv = 0.35. Both models were externally validated by predicting the activity values of the corresponding test set, and the tautomer II model, which showed the best external prediction performance, was used to predict the biological activity responses of the compounds that were not evaluated in the anti-MTB trials due to poor solubility, pointing out D21 for further solubility studies to attempt to determine its actual biological activity.

Highly predictive hologram QSAR models of nitrile-containing cruzain inhibitors

The HQSAR, molecular docking, and ROCS were applied to a data-set of 57 cruzain inhibitors. The best HQSAR model (q2 = .70, r2 = .95, = .62, = .09 and = .26), employing well-balanced, diverse training (40) and test (17) sets, was obtained using atoms (A), bonds (B), and hydrogen (H) as fragment distinctions and 6–9 as fragment sizes. This model was then used to predict the unknown potencies of 121 compounds (the V1 database), giving rise to a satisfactory predictive r2 value of .65 (external validation). By employing an extra external data-set comprising 1223 compounds (the V3 database) either retrieved from the ChEMBL or CDD databases, an overall ROC AUC score well over .70 was obtained. The contribution maps obtained with the best HQSAR model (model 3.4) are in agreement with the predicted binding mode and with the biological potencies of the studied compounds. We also screened these compounds using the ROCS method, a Gaussian-shape volume filter able to identify quickly the shapes that match a query molecule. The area under the curve (AUC) obtained with the ROC curves (ROC AUC) was .72, indicating that the method was very efficient in distinguishing between active and inactive cruzain inhibitors. These set of information guided us to propose novel cruzain inhibitors to be synthesized. Then, the best HQSAR model obtained was used to predict the pIC50 values of these new compounds. Some compounds identified using this method have shown calculated potencies higher than those which have originated them.

Understanding PPAR-δ affinity and selectivity using hologram quantitative structure-activity modeling, molecular docking and GRID calculations.

Type 2 diabetes mellitus and metabolic syndrome are two diseases related to disorders of lipid and carbohydrate metabolism and insulin resistance. Peroxisome proliferator-activated receptors (PPARs) are a class of nuclear receptors that control the metabolism of lipids/carbohydrates and are considered targets for both diseases. PPAR affinity and selectivity are critical points to design drug candidates with appropriated pharmacodynamic/kinetic profiles. Hologram quantitative structure-activity relationships studies were conducted, as well molecular docking and molecular interaction field calculations, in order to explain affinity and selectivity of selected compounds. The constructed hologram quantitative structure-activity relationship models are robust and predictive (values of q2 and r2test above 0.70). The quantitative structure-activity relationship models and docking/GRID analyses indicated that carboxyl group of indole-sulfonamide derivatives could interact at helix-3 region, being considered important point of PPAR-δ selectivity.

Novel ligands of Choline Acetyltransferase designed by in silico molecular docking, hologram QSAR and lead optimization.

Recent reports have brought back the acetylcholine synthesizing enzyme, choline acetyltransferase in the mainstream research in dementia and the cholinergic anti-inflammatory pathway. Here we report, a specific strategy for the design of novel ChAT ligands based on molecular docking, Hologram Quantitative Structure Activity Relationship (HQSAR) and lead optimization. Molecular docking was performed on a series of ChAT inhibitors to decipher the molecular fingerprint of their interaction with the active site of ChAT. Then robust statistical fragment HQSAR models were developed. A library of novel ligands was generated based on the pharmacophoric and shape similarity scoring function, and evaluated in silico for their molecular interactions with ChAT. Ten of the top scoring invented compounds are reported here. We confirmed the activity of α-NETA, the only commercially available ChAT inhibitor, and one of the seed compounds in our model, using a new simple colorimetric ChAT assay (IC50 ~ 88 nM). In contrast, α-NETA exhibited an IC50 of ~30 μM for the ACh-degrading cholinesterases. In conclusion, the overall results may provide useful insight for discovering novel ChAT ligands and potential positron emission tomography tracers as in vivo functional biomarkers of the health of central cholinergic system in neurodegenerative disorders, such as Alzheimer’s disease.

Hologram QSAR study on the electrophoretic mobility of aromatic acids

The quantitative structure property relationship (QSPR) for the electrophoretic mobility of aromatic acids was investigated. Hologram QSAR (HQSAR) method was used to build the QSPR model. The fragment parameters of the developed HQSAR model are fragment size: 2–5; fragment length: 307; fragment distinctions: atoms, bonds and connections. The predictive ability of the developed model was assessed with external test validation and leave-one-out cross validation. The results of the two validations demonstrate that the developed HQSAR model is practicable for describing the quantitative relationship between the hologram information and electrophoretic mobility of the investigated aromatic acids. And the contribution map of the developed HQSAR model can graphically illustrate the influence of molecular structure on the electrophoretic mobility of the investigated aromatic acids. Obviously, HQSAR method is fast, practicable and promising for predicting and studying the electrophoretic mobility of aromatic acids.

Synthesis, cytotoxic activity evaluation and HQSAR study of novel isosteviol derivatives as potential anticancer agents

A series of novel isosteviol derivatives bearing amino alcohol and thiourea fragments have been stereo-selectively synthesized and screened for their in vitro cytotoxic activities against three human cancer cell lines (HCT-116, HGC-27 and JEKO-1). The results demonstrated that these compounds exhibited prominent cytotoxicities. Especially, the compound Iw displayed the most potent anticancer activities against HCT-116 cell with IC50 value of 1.450 μM. On the basis of this bioassay results, these derivatives were further investigated by the hologram quantitative structure–activity relationship (HQSAR) technique. The optimal HQSAR model with q2 = 0.663, r2 = 0.895, SEE = 0.179 was generated using A/B/H/Ch as fragment distinction parameters and 4–7 as fragment size. This model was employed to predict the cytotoxic activities of test set compounds, and the predicted values were in good agreement with the experimental results. The contribution maps derived from the optimal model explained the individual atomic contribution to the total activity of single molecule.

Fragment-Based Hologram QSAR Studies on a Series of 2,4-Dioxopyrimidine-1-Carboxamides As Highly Potent Inhibitors of Acid Ceramidase.

A series of structurally related 2,4-dioxopyrimidine-1-carboxamide derivatives as highly potent inhibitors against acid ceramidase were subjected to hologram quantitative structure-activity relationship (HQSAR) analysis. A training set containing 24 compounds served to establish the HQSAR model. The best HQSAR model was generated using atoms, bond, connectivity, donor and acceptor as fragment distinction and 3-6 as fragment size with six components showing cross-validated q2 value of 0.834 and conventional r2 value of 0.965. The model was then employed to predict the potency of test set compounds that were excluded in the training set, and a good agreement between the experimental and predicted values was observed exhibiting the powerful predictable capability of this model [Formula: see text]. Atom contribution maps indicate that the electron-withdrawing effects at position 5 of the uracil ring, the preferential acyl substitution at N3 position and the substitution of eight-carbon alkyl chain length at N1 position predominantly contribute to the inhibitory activity. Based upon these key structural features derived from atom contribution maps, we have designed novel inhibitors of acid ceramidase possessing better inhibitory activity.

Pharmacophore generation, atom-based 3D-QSAR, HQSAR and activity cliff analyses of benzothiazine and deazaxanthine derivatives as dual A2A antagonists/MAO‑B inhibitors

Dual inhibition of A2A and MAO-B is an emerging strategy in neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). In this study, atom-based three-dimensional quantitative structure–activity relationship (3D-QSAR) and hologram quantitative structure–activity relationship (HQSAR) models were generated with benzothiazine and deazaxanthine derivatives. Based on activity against A2A and MAO-B, two statistically significant 3D-QSAR models (r2 = 0.96, q2 = 0.76 and r2 = 0.91, q2 = 0.63) and HQSAR models (r2 = 0.93, q2 = 0.68 and r2 = 0.97, q2 = 0.58) were developed. In an activity cliff analysis, structural outliers were identified by calculating the Mahalanobis distance for a pair of compounds with A2A and MAO-B inhibitory activities. The generated 3D-QSAR and HQSAR models, activity cliff analysis, molecular docking and dynamic studies for dual target protein inhibitors provide key structural scaffolds that serve as building blocks in designing drug-like molecules for neurodegenerative diseases.

Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives.

Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer's disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r (2)=0.988, q (2)=0.757, ONC=6; r (2)=0.966, q (2)=0.645, ONC=5; and r (2)=0.957, q (2)=0.736, ONC=6. The predictabilities were validated using an external test sets, and the predictive r (2) values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors.

QSAR analysis of thiolactone derivatives using HQSAR, CoMFA and CoMSIA

The development of resistant malaria and lethality of the disease demands the search for new therapeutic candidates. In this line-up, thiolactone was identified as the potential lead structure and subjected to hologram quantitative structure–activity relationship (HQSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Overall, the QSAR results shows that the LOO cross-validated q2 values of HQSAR, CoMFA and CoMSIA models are 0.791, 0.737 and 0.753, respectively. According to HQSAR, the hydrogen bond donor and acceptor were found to play an important role in governing antimalarial activity of thiolactone derivatives. The fragment contribution map of HQSAR, and contour maps of CoMFA and CoMSIA showed the presence of an electronegative group at the fifth position, and a bulky group at the third and fourth positions of the thiolactone ring, positively contributing to antimalarial activity. Furthermore, molecular docking was performed to analyze the binding mode of newly designed thiolactones with the active site residues of pf KAS I/II. The prediction of newly designed thiolactone molecules based on QSAR and docking score are in good accordance with each other. Therefore the ligand-based QSAR models and target structure-based docking model developed in this study may be successfully utilized for the design of new antimalarial agents.

Fragment-Based QSAR and Structural Analysis of a Series of Hydroxyethylamine Derivatives as HIV-1 Protease Inhibitors.

HIV-1 protease is a key enzyme for viral maturation because it cleaves precursor polypeptides into mature structural and functional proteins. The introduction of protease inhibitors into therapy in the mid-1990s has dramatically changed the AIDS panorama worldwide. However, resistance to currently available protease inhibitors remains a serious challenge that must be overcome. Herein, we report a fragment-based QSAR study of a series of highly potent HIV-1 protease inhibitors, as well as the structural basis of their binding affinity. Hologram QSAR (HQSAR) analyses were performed, resulting in robust statistical models that consistently correlated the bioactivity profile with the two dimensional molecular descriptors. The robustness of the best model was assessed based on the correlation coefficients (q(2) = 0.70 and r(2) = 0.90), as well as the prediction of the activity of an external test set (r(2) pred = 0.75). Structure-based molecular modeling studies were performed to investigate the binding mode of the best inhibitor in the active site of the enzyme. The HQSAR model and the structural findings provide valuable insights for the rational design of structurally related HIV-1 protease inhibitors.

Hologram Quantitative Structure Activity Relationship Analysis of JNK Antagonists

c-Jun N-terminal kinase-3 (JNK3) is a member of the mitogen-activated protein kinase family (MAPK), and plays an important role in neurological disorders. Therefore, identification of selective JNK3 inhibitor may contribute towards neuroprotection therapies. In this work, we performed hologram quantitative structure-activity relationship (HQSAR) on a series of thiophene trisubstituted derivatives. The best predictions were obtained for HQSAR model with q2 = 0.628 and r2 = 0.986. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. HQSAR result showed that atom, bond and chirality descriptors play an important role in JNK3 activity and also shows that electronegative groups is highly favourble to enhance the biological activity. Our results could be useful to design novel and selective JNK3 inhibitors.

HQSAR and topomer CoMFA for predicting melanocortin-4 receptor binding affinities of trans-4-(4-chlorophenyl) pyrrolidine-3-carboxamides

Hologram quantitative structure–activity relationships (HQSAR) and topomer comparative molecular field analysis (CoMFA) were implemented on a group of 73 trans-4-(4-chlorophenyl) pyrrolidine-3-carboxamides of piperazinecyclohexane compounds, and their binding affinities to the melanocortin-4 receptor (MC4R) were determined. The most effective HQSAR model was obtained using atoms and bonds as fragment distinction, and its cross-validated (q2) and non-cross-validated (r2) correlation coefficients were 0.665 and 0.907, respectively. Several external validation parameters (i.e., four ways to calculate the correlation coefficients of the test set samples), namely, QF12, QF22, QF32, and CCC, were 0.781, 0.766, 0.789, and 0.901, respectively. Topomer CoMFA models were built based on three fragment cutting modes. The most effective topomer CoMFA (r2=0.850, q2=0.436, QF12=0.784, QF22=0.779, QF32=0.751, and CCC=0.894) used the amide bond as the cut bond. Meanwhile, the predictive capabilities of the most effective HQSAR and topomer CoMFA models were tested and verified via an external test set with 15 compounds, and then compared with those of previously reported models. Molecular docking was employed to validate the results of the HQSAR and topomer CoMFA models. Moreover, the graphical results of HQSAR, topomer CoMFA, and docking were analyzed and used as guides in designing new MC4R ligands. The results of this work would be useful in chemical library design, virtual screening, and high-throughput screening.

A Predictive HQSAR Model for a Series of Tricycle Core Containing MMP-12 Inhibitors with Dibenzofuran Ring.

MMP-12 is a member of matrix metalloproteinases (MMPs) family involved in pathogenesis of some inflammatory based diseases. Design of selective matrix MMPs inhibitors is still challenging because of binding pocket similarities among MMPs family. We tried to generate a HQSAR (hologram quantitative structure activity relationship) model for a series of MMP-12 inhibitors. Compounds in the series of inhibitors with reported biological activity against MMP-12 were used to construct a predictive HQSAR model for their inhibitory activity against MMP-12. The HQSAR model had statistically excellent properties and possessed good predictive ability for test set compounds. The HQSAR model was obtained for the 26 training set compounds showing cross-validated q 2 value of 0.697 and conventional r 2 value of 0.986. The model was then externally validated using a test set of 9 compounds and the predicted values were in good agreement with the experimental results (r pred 2 = 0.8733). Then, the external validity of the model was confirmed by Golbraikh-Tropsha and r m 2 metrics. The color code analysis based on the obtained HQSAR model provided useful insights into the structural features of the training set for their bioactivity against MMP-12 and was useful for the design of some new not yet synthesized MMP-12 inhibitors.

Combined 2D and 3D-QSAR, molecular modelling and docking studies of pyrazolodiazepinones as novel phosphodiesterase 2 inhibitors

Selective inhibition of phosphodiesterase 2 (PDE2) in cells where it is located elevates cyclic guanosine monophosphate (cGMP) and acts as novel analgesic with antinociceptive activity. Three-dimensional quantitative structure–activity relationship (QSAR) studies for pyrazolodiazepinone inhibitors exhibiting PDE2 inhibition were performed using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and Topomer CoMFA, and two-dimensional QSAR study was performed using a Hologram QSAR (HQSAR) method. QSAR models were generated using training set of 23 compounds and were validated using test set of nine compounds. The optimum partial least squares (PLS) for CoMFA-Focusing, CoMSIA-SDH, Topomer CoMFA and HQSAR models exhibited good ‘leave-one-out’ cross validated correlation coefficient (q2) of 0.790, 0.769, 0.840 and 0.787, coefficient of determination (r2) of 0.999, 0.964, 0.979 and 0.980, and high predictive power (r2pred) of 0.796, 0.833, 0.820 and 0.803 respectively. Docking studies revealed that those inhibitors able to bind to amino acid Gln859 by cGMP binding orientation called ‘glutamine-switch’, and also bind to the hydrophobic clamp of PDE2 isoform, could possess high selectivity for PDE2. From the results of all the studies, structure–activity relationships and structural requirements for binding to active site of PDE2 were established which provide useful guidance for the design and future synthesis of potent PDE2 inhibitors.

Extracting structural requirements for activity of GPR119 agonists: a hologram quantitative structure activity relationship (HQSAR) study

GPR119 is a potential target for the treatment of diabetes mellitus. GPR119 agonists minimize the side-effects observed with sulphonyl ureas and glucagon-like peptide 1 analogs. Various reported GPR119 agonists from various patents were selected for the study and a 2D-QSAR study (HQSAR) was carried out. Fifty-five molecules were selected for the study. The study was performed on a training set of 40 structurally diverse molecules with reported biological activity. The most significant HQSAR model (q2 = 0.87, r2 = 0.99) was obtained using atoms, bond, connection, and acceptor and donor as fragment distinction. The fragment size was kept at 4–7. The predictive ability of the model was evaluated by an external test set containing 15 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The important fragments determined by the study were used to design new drug candidates having increased biological activity and comparable physicochemical properties.

Cholesteryl ester transfer protein inhibitors in coronary heart disease: Validated comparative QSAR modeling of N, N-disubstituted trifluoro-3-amino-2-propanols

Cholesteryl ester transfer protein (CETP) converts high density lipoprotein cholesterol to low density lipoproteins. It is a promising target for treatment of coronary heart disease. Two dimensional quantitative structure activity relationship (2D-QSAR), hologram QSAR (HQSAR) studies and comparative molecular field analysis (CoMFA) as well as comparative molecular similarity analysis (CoMSIA) were performed on 104 CETP inhibitors. The statistical qualities of generated models were justified by internal and external validation, i.e., q2 and R2pred respectively. The best 2D-QSAR model was obtained with q2 and R2pred values of 0.794 and 0.796 respectively. The 2D-QSAR study suggests that unsaturation, branching and van der Waals volumes may play important roles. The HQSAR model showed q2 and R2pred values of 0.628 and 0.550 respectively. Similarly, CoMFA model showed q2 and R2pred values of 0.707 and 0.755 respectively whereas CoMSIA model was obtained with q2 and R2pred values of 0.696 and 0.703 respectively. CoMFA and CoMSIA studies indicate that steric factors are important at substituted phenoxy and tetrafluoroethoxy groups whereas electropositive factors play important role at difluoromethyl group. The results of 3D-QSAR studies validate those of 2D-QSAR and HQSAR studies as well as the earlier observed SAR data. Current work may help to develop better CETP inhibitors.

CoMFA, CoMSIA and HQSAR Studies of Acetylcholinesterase Inhibitors

A quantitative structure-activity relationship (QSAR) study has been carried out on acetylcholinesterase (AChE) inhibitors with comparative field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR). In order to investigate the effect of alignment on modeling and find out the best alignment strategy, three different alignment rules were applied to generate CoMFA and CoMSIA models. Statistical results of the highly significant models (CoMFA q² = 0.748, r² =0.996, predicted r² =0.789; CoMSIA q² =0.755, r² =0.973, predicted r² = 0.706; HQSAR q² = 0.884, r² = 0.973, predicted r² = 0.734) reveal considerable predictive ability. Analysis of the contour maps of CoMFA and CoMSIA models and the atomic contribution maps of HQSAR model may contribute to develop novel and potential AChE inhibitors.

Computational studies of TGF-βRI (ALK-5) inhibitors: Analysis of the binding interactions between ligand–receptor using 2D and 3D techniques

ALK-5 (Activin-Like Kinase 5) is a biological receptor involved in a variety of pathological processes such as cancer and fibrosis. ALK-5 receptor propagates an intracellular signaling that forms a protein complex capable of reaching the nucleus and modulating the gene transcription. In the present study, comparative molecular field analysis (CoMFA) and hologram quantitative structure–activity relationship (HQSAR) studies were conducted on a series of potent ALK-5 inhibitors. Significant correlation coefficients (CoMFA, r2=0.99 and q2=0.85; HQSAR, r2=0.92 and q2=0.72) were obtained, indicating the predictive potential of the 2D and 3D models for untested compounds. The models were then used to predict the potency of a test set, and the predicted values from the HQSAR and CoMFA models were in good agreement with the experimental results. The final QSAR models, along with the information obtained from 3D (steric and electrostatic) contour maps and 2D contribution maps, can be useful for the design of novel bioactive ligands.

Exploring structural requirements of aurone derivatives as antimalarials by validated DFT-based QSAR, HQSAR, and COMFA–COMSIA approach

Chloroquine resistance is nowadays a great problem in malaria. Aurone derivatives were effective against chloroquine resistant parasite. Validated density functional theory (DFT)-based chemometric modeling, hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity analysis (CoMSIA) studies were conducted on 35 aurone derivatives having antimalarial activity. 2D-QSAR models were developed on the training sets by Y-based ranking method. This model was validated on 50 pairs of the test and the training sets by k-Means cluster analysis method. HQSAR, CoMFA, and CoMSIA models were validated by standard techniques and each method validates the DFT-based 2D-QSAR study and in turn validates the earlier observed structural activity relationship data as well as each other. DFT-based 2D-QSAR model suggests that the increase of Mulliken charge at C14 and HOMO density located on C11 may be conducive to antimalarial activity. Ethyl group attached to C14 and the increase of the value of chemical potential may be beneficial for antimalarial activity. Methoxy fragment is important for better antimalarial activity by HQSAR study. CoMFA analysis shows a favorable steric green region is located near C14 whereas the unfavorable yellow region is far away from C14. A large blue region located near C14 indicates the positively charged groups are favorable at this position. CoMSIA steric features correlates well with the CoMFA steric features. CoMSIA study suggests the bulky hydrophobic substitution at C14 is necessary for antimalarial activity. These results may be utilized to obtain potential antimalarial molecules.