Hologram QSAR Research Articles

Asymmetric bioreduction of β-ketoesters derivatives by Kluyveromyces marxianus: influence of molecular structure on the conversion and enantiomeric excess.

This study presents the bioreduction of six β-ketoesters by whole cells of Kluyveromyces marxianus and molecular investigation of a series of 13 β-ketoesters by hologram quantitative structure-activity relationship (HQSAR) in order to relate with conversion and enantiomeric excess of β-stereogenic-hydroxyesters obtained by the same methodology. Four of these were obtained as (R)-configuration and two (S)-configuration, among them four compounds exhibited >99% enantiomeric excess. The β-ketoesters series LUMO maps showed that the β-carbon of the ketoester scaffold are exposed to undergo nucleophilic attack, suggesting a more favorable β-carbon side to enzymatic reduction based on adopted molecular conformation at the reaction moment. The HQSAR method was performed on the β-ketoesters derivatives separating them into those provided predominantly (R)- or (S)-β-hydroxyesters. The HQSAR models for both (R)- and (S)-configuration showed high predictive capacity. The HQSAR contribution maps suggest the importance of β-ketoesters scaffold as well as the substituents attached therein to asymmetric reduction, showing a possible influence of the ester group carbonyl position on the molecular conformation in the enzyme catalytic site, exposing a β-carbon side to the bioconversion to (S)- and (R)-enantiomers.

Quionolone carboxylic acid derivatives as HIV‐1 integrase inhibitors: Docking‐based HQSAR and topomer CoMFA analyses

Quionolone carboxylic acid derivatives as inhibitors of HIV-1 integrase were investigated as a potential class of drugs for the treatment of acquired immunodeficiency syndrome (AIDS). Hologram quantitative structure-activity relationships (HQSAR) and translocation comparative molecular field vector analysis (topomer CoMFA) were applied to a series of 48 quionolone carboxylic acid derivatives. The most effective HQSAR model was obtained using atoms and bonds as fragment distinctions: cross-validation q2 = 0.796, standard error of prediction SDCV = 0.36, the non-cross-validated r2 = 0.967, non-cross validated standard error SD = 0.17, the correlation coefficient of external validation Qext2 = 0.955, and the best hologram length HL = 180. topomer CoMFA models were built based on different fragment cutting models, with the most effective model of q2 = 0.775, SDCV = 0.37, r2 = 0.967, SD = 0.15, Qext2 = 0.915, and F = 163.255. These results show that the models generated form HQSAR and topomer CoMFA were able to effectively predict the inhibitory potency of this class of compounds. The molecular docking method was also used to study the interactions of these drugs by docking the ligands into the HIV-1 integrase active site, which revealed the likely bioactive conformations. This study showed that there are extensive interactions between the quionolone carboxylic acid derivatives and THR80, VAL82, GLY27, ASP29, and ARG8 residues in the active site of HIV-1 integrase. These results provide useful insights for the design of potent new inhibitors of HIV-1 integrase.

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.

Combined HQSAR, topomer CoMFA, homology modeling and docking studies on triazole derivatives as SGLT2 inhibitors.

Sodium-glucose cotransporter 2 (SGLT2) is a promising target for diabetes therapy. We aimed to develop computational approaches to identify structural features for more potential SGLT2 inhibitors. In this work, 46 triazole derivatives as SGLT2 inhibitors were studied using a combination of several approaches, including hologram quantitative structure-activity relationships (HQSAR), topomer comparative molecular field analysis (CoMFA), homology modeling, and molecular docking. HQSAR and topomer CoMFA were used to construct models. Molecular docking was conducted to investigate the interaction of triazole derivatives and homology modeling of SGLT2, as well as to validate the results of the HQSAR and topomer CoMFA models. The most effective HQSAR and topomer CoMFA models exhibited noncross-validated correlation coefficients of 0.928 and 0.891 for the training set, respectively. External predictions were made successfully on a test set and then compared with previously reported models. The graphical results of HQSAR and topomer CoMFA were proven to be consistent with the binding mode of the inhibitors and SGLT2 from molecular docking. The models and docking provided important insights into the design of potent inhibitors for SGLT2.

Molecular modeling and structure-activity relationships for a series of benzimidazole derivatives as cruzain inhibitors.

Chagas disease is endemic in Latin America and no effective treatment is available. Efforts in drug research have focused on several enzymes from Trypanosoma cruzi, among which cruzain is a validated pharmacological target. Chemometric analyses were performed on the data set using the hologram quantitative structure-activity relationship, comparative molecular field analysis and comparative molecular similarity index analysis methods. Docking simulations were executed using the crystallographic structure of cruzain in complex with a benzimidazole inhibitor. The top-scoring enzyme-inhibitor complexes were selected for the development of the 3D quantitative structure-activity relationship (QSAR) models and to assess the inhibitor binding modes and intermolecular interactions. Benzimidazole derivatives as cruzain inhibitors were used in molecular docking and QSAR studies. Significant statistical indicators were obtained, and the best models demonstrated high predictive ability for an external test set (r 2pred = 0.65, 0.94 and 0.82 for hologram QSAR, comparative molecular field analysis and comparative molecular similarity index analysis, respectively). Additionally, the graphical information of the chemometric analyses demonstrated substantial complementarity with the enzyme-binding site. These results demonstrate the relevance of the QSAR models to guide the design of structurally related benzimidazole derivatives with improved potency.

Flavones with inhibitory effects on glycogen synthase kinase 3β

Because glycogen synthase kinase-3 (GSK-3) activity is linked to various human diseases, it has been targeted in new drug development. Flavonoids, including luteolin, apigenin, and quercetin, inhibit GSK-3β; however, the relationships between their structural properties and inhibitory effects are unclear. We measured the inhibitory effects of 34 flavonoid derivatives on GSK-3β and calculated hologram quantitative structure–activity relationships to provide information on pharmacophores for designing novel compounds with better activities. The in vitro binding effect of flavonoids was confirmed using Western blotting for myricetin, which showed the best inhibitory activity, and the binding mode between myricetin and GSK-3β was elucidated using in silico docking.

An integrated ligand-based modelling approach to explore the structure-property relationships of influenza endonuclease inhibitors

Influenza endonuclease plays important role in the viral transcription and translation processes. Inhibition of endonuclease enzyme may be an interesting choice to restrict influenza infection. This current study deals with validated multi-chemometric modelling approaches namely regression-based and classification-based quantitative structure-activity relationships (QSARs), hologram QSAR, comparative molecular similarity analysis (CoMSIA), Open3DQSAR study and pharmacophore mapping to identify the structural and physicochemical requirements along with the chemico-biological interactions of pyridinones and pyranones for anti-endonuclease activity. The results suggest that the pyridinone scaffold is more preferable than the pyranone ring. The keto function at 4th position and aryl tetrazole substitution at 1st position of the parent moiety may be important for endonuclease inhibition. Hydroxyl group at 5th position of the parent ring may act as hydrogen bond acceptor feature. The steric substituent is suitable at 2nd position whereas hydrophobic substitution is found to be unfavourable at this position. Bulky hydrophobic substituents are not preferred at the 3rd position of the parent moiety. The information revealed from these integrated ligand-based modelling methods may provide useful informations for designing newer potential anti-influenza agents in future.

2D and 3D QSAR Studies on a Series of Antichagasic Fenarimol Derivatives

Chagas disease is one of the most important neglected tropical diseases. Endemic in Latin America, the disease is a global public health problem, affecting several countries in North America, Europe, Asia and Oceania. The disease affects around 8-10 million people worldwide and the limited treatments available present low efficacy and severe side effects, highlighting the urgent need for new therapeutic options. In this work, the authors developed QSAR models for a series of fenarimol derivatives exhibiting anti-T. cruzi activity. The models were constructed using the Hologram QSAR (HQSAR), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. The QSAR models presented substantial predictive ability for a series of test set compounds (HQSAR, r2pred = 0.66; CoMFA, r2pred = 0.82; and CoMSIA, r2pred = 0.76), and were valuable to identify key structural features related to the observed trypanocidal activity. The results reported herein are useful for the design of novel derivatives having improved antichagasic properties.

Comparative QSAR studies using HQSAR, CoMFA, and CoMSIA methods on cyclic sulfone hydroxyethylamines as BACE1 inhibitors

The inhibition of β-secretase (BACE1) is currently the main pharmacological strategy available for Alzheimer’s disease (AD). 2D QSAR and 3D QSAR analysis on some cyclic sulfone hydroxyethylamines inhibitors against β-secretase (IC50: 0.002–2.75μM) were carried out using hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods. The best model based on the training set was generated with a HQSAR q2 value of 0.693 and r2 value of 0.981; a CoMFA q2 value of 0.534 and r2 value of 0.913; and a CoMSIA q2 value of 0.512 and r2 value of 0.973. In order to gain further understand of the vital interactions between cyclic sulfone hydroxyethylamines and the protease, the analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the BACE1. The final QSAR models could be helpful in the design and development of novel active BACE1 inhibitors.

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.

Syntheses, cytotoxic activity evaluation and HQSAR study of 1,2,3-triazole-linked isosteviol derivatives as potential anticancer agents

A series of novel 1,2,3-triazole-linked isosteviol derivatives were designed and synthesized via Huisgen-click reaction. Their cytotoxicities in vitro against HCT-116 and JEKO-1 cells were screened. The preliminary bioassays indicated that most of the title compounds exhibited noteworthy cytotoxic activities. Particularly, the compound 10b revealed the most potent inhibitory activities against HCT-116 cells with IC50 value of 2.987±0.098μM, which was better than that (3.906±0.261μM) of positive control cisplatin. On the basis of these bioactivity data, hologram quantitative structure–activity relationship (HQSAR) was performed, and a statistically reliable model with good predictive power (r2=0.848, q2=0.544 and R2pred=0.982) was achieved. Additionally, the contribution maps derived from the optimal model explained the individual atomic contributions to the activity for each molecule.

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.

A Fragment-Based Approach for the Computational Prediction of the Nonspecific Binding of Drugs to Hepatic Microsomes.

Correction for the nonspecific binding (NSB) of drugs to liver microsomes is essential for the accurate measurement of the kinetic parameters Km and Ki, and hence in vitro-in vivo extrapolation to predict hepatic clearance and drug-drug interaction potential. Although a number of computational approaches for the estimation of drug microsomal NSB have been published, they generally rely on compound lipophilicity and charge state at the expense of other physicochemical and chemical properties. In this work, we report the development of a fragment-based hologram quantitative structure activity relationship (HQSAR) approach for the prediction of NSB using a database of 132 compounds. The model has excellent predictivity, with a noncross-validated r2 of 0.966 and cross-validated r2 of 0.680, with a predictive r2 of 0.748 for an external test set comprising 34 drugs. The HQSAR method reliably predicted the fraction unbound in incubations of 95% of the training and test set drugs, excluding compounds with a steroid or morphinan 4,5-epoxide nucleus. Using the same data set of compounds, performance of the HQSAR method was superior to a model based on logP/D as the sole descriptor (predictive r2 for the test set compounds, 0.534). Thus, the HQSAR method provides an alternative approach to laboratory-based procedures for the prediction of the NSB of drugs to liver microsomes, irrespective of the drug charge state (acid, base, or neutral).

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.

Insight into the Structural Requirements of Theophylline-Based Aldehyde Dehydrogenase lAl (ALDHlAl) Inhibitors Through Multi-QSAR Modeling and Molecular Docking Approaches.

Over expression of aldehyde dehydrogenase (ALDH1A1) is one of the vital hallmarks of the self-renewal and differentiational cancer stem cells (CSCs). Till now, no selective ALDH1A1 inhibitor is commercially available in the market. So there is an urgent need to explore some novel molecules which can selectively inhibit ALDH1A1 to combat cancer. Presently, our work deals with the development of QSAR models of some theophylline-based molecules by conventional 2D-QSAR, hologram QSAR (HQSAR), and Bayesian classification modeling. The descriptors identified from these QSAR models give avenues to modulate the structure of theophylline-based compounds to a desirable biological end point. Molecular docking study reveals the selectivity of these molecules towards ALDH1A1 (PDB: 4WP7) and important binding residues (GLY 125, 458; THR 129; TRP 178; TYR 297; PHE 171, 466; VAL 174, 460; MET 175; HIS 293 etc.) for the interaction with the receptors. The current study may help to design novel compounds as selective ALDH1A1 inhibitors.

HQSAR and molecular docking studies of furanyl derivatives as adenosine A2A receptor antagonists

Structure- and ligand-based computational design strategies were used to understand the molecular requirements of furanyl derivatives 2-(furan-2-yl)-[1,2,4]triazolo[1,5-f]pyrimidin-5-amines (TfPAs), 2-(furan-2-yl)-[1,2,4]triazolo[1,5-a]pyrazin-8-amine (TaPAs), and 2-(furan-2-yl)-[1,2,4]triazolo[1,5-a][1,3,5]triazin-7-amines (TTAs) as adenosine A2A receptor (A2AR) antagonists. First, we studied the structure–activity relationship of the selected compounds using hologram quantitative structure–activity relationship (HQSAR) methodology. The best model (training set included 67 compounds) included fragment parameters such as atoms (A), bonds (B), connections (C), and donors/acceptors groups (DA) and had a good q2 value of 0.776 including size fragments of 7–10 and the hologram length of 199. It also predicted adequately the compounds contained in the test set (10 compounds). In addition, we studied the binding orientations of the higher and less active compounds using flexible molecular docking. We found orientations that are in agreement with previous reports.

QSAR models for predicting octanol/water and organic carbon/water partition coefficients of polychlorinated biphenyls

Quantitative structure–property relationship modelling can be a valuable alternative method to replace or reduce experimental testing. In particular, some endpoints such as octanol–water (KOW) and organic carbon–water (KOC) partition coefficients of polychlorinated biphenyls (PCBs) are easier to predict and various models have been already developed. In this paper, two different methods, which are multiple linear regression based on the descriptors generated using Dragon software and hologram quantitative structure–activity relationships, were employed to predict suspended particulate matter (SPM) derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of 209 PCBs. The predictive ability of the derived models was validated using a test set. The performances of all these models were compared with EPI Suite™ software. The results indicated that the proposed models were robust and satisfactory, and could provide feasible and promising tools for the rapid assessment of the SPM derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of PCBs.

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.

QSPR models for predicting generator-column-derived octanol/water and octanol/air partition coefficients of polychlorinated biphenyls

Octanol/water (KOW) and octanol/air (KOA) partition coefficients are two important physicochemical properties of organic substances. In current practice, KOW and KOA values of some polychlorinated biphenyls (PCBs) are measured using generator column method. Quantitative structure–property relationship (QSPR) models can serve as a valuable alternative method of replacing or reducing experimental steps in the determination of KOW and KOA. In this paper, two different methods, i.e., multiple linear regression based on dragon descriptors and hologram quantitative structure–activity relationship, were used to predict generator-column-derived log KOW and log KOA values of PCBs. The predictive ability of the developed models was validated using a test set, and the performances of all generated models were compared with those of three previously reported models. All results indicated that the proposed models were robust and satisfactory and can thus be used as alternative models for the rapid assessment of the KOW and KOA of PCBs.