Hologram Quantitative Structure-activity Relationship Model Research Articles

Hologram quantitative structure-activity relationship on the gas chromatographic retention index of plant essential oil constituents

The gas chromatography retention index (RI) is an important parameter for the identification of different types of compounds in the field of chromatographic analysis; however, the experimental collection of RI values is a extremely cumbersome process. Thus, there is an urgent need for the establishment of a simple, efficient, and accurate model for the prediction of the RI values of compounds. In this study, first, the experimental RI values for 60 plant essential oil constituents were obtained. Next, a model describing the hologram quantitative structure-activity relationship (HQSAR) between the structural properties of the essential oil constituents and their RI values was investigated and constructed. The optimal HQSAR model was established by setting the model parameters "fragment size", "fragment distinction", "hologram length" and "principal components" to "1-4", "C, Ch", "199", and "4", respectively. Finally, the predictive ability of the model was verified using external test set validation and leave-one-out cross-validation (LOO-CV). The experimental results were as follows, the root mean square error of prediction (RMSEP), predictive determination coefficient ([Formula: see text]), concordance correlation coefficient (CCC), and mean relative error (MRE) for external test set validation were 40.45, 0.984, 0.968, and 2.20%, respectively. Meanwhile, the root mean square error of cross validation (RMSECV) and MRE for LOO-CV were 72.56 and 4.17%, respectively. These findings demonstrate that the established HQSAR model has a good predictive ability and can accurately predict the RI values of plant essential oil constituents. In addition, the molecular contribution maps of the HQSAR model revealed that the RI values of aromatic compounds increase when hydroxyl groups are connected to their alkyl chains. Aliphatic compounds feature long chain alkyl groups, which can lead to an increase in RI values. The above phenomena highlight the promising application prospects of HQSAR for studying the RI values of plant essential oil constituents. Therefore, this study provides a reliable theoretical basis for predicting the RI values of other essential oil constituents.

Convergent QSAR Models for the Prediction of Cruzain Inhibitors.

Chagas disease is a parasitosis caused by Trypanosoma cruzi. Cruzain, the major cysteine protease from T. cruzi, is an excellent therapeutic target in the search for antichagasic drugs. It is important in the role of cell invasion, replication, differentiation, and metabolism of the parasite. In this work, we developed and assessed multiple quantitative structure-activity relationship (QSAR) models for a set of 61 cruzain inhibitors. These models include two-dimensional (2D) QSAR, three-dimensional (3D) QSAR, such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and Hologram QSAR (HQSAR). In total, we generated 10 major and 114 minor model variations. Molecular docking was used to successfully align the molecules. All CoMFA and CoMSIA models, which incorporate multiple fields, demonstrated robustness in our analysis. Steric fields exhibited satisfactory convergence in the contour maps, while the electrostatic field converged into a single small region. The HQSAR model taking into consideration only Atoms and Connectivity, with fragment sizes ranging from two to five atoms, was considered the best of the HQSAR variations, despite exhibiting a higher level of deviance. In total, 78 model variations meet the minimum requirements to be considered acceptable. We found that using as few as five descriptors it is possible to obtain robust results with 2D-QSAR. Models such as Random Forest, Tree Ensemble, Linear Regression, and HQSAR are recommended for working with large data sets, while the 3D-QSAR models are intended to study the geometry of the ligands, to optimize them into new and better performing antichagasics. Virtual Screening of a set of hydrazones, guided by the top-performing models, identified promising candidates for experimental validation. Among them, dv007 and dv015 exhibited consistently high predicted pIC50 values (7.26 and 7.24, respectively), making them compelling candidates for further drug development.

Hologram quantitative structure-activity relationship guided in silicon molecular design of phenothiazine dye sensitizers for dye-sensitized solar cells

Hologram quantitative structure–activity relationship (HQSAR) method was successfully introduced into the structure-performance study on the phenothiazine dye sensitizers of dye-sensitized solar cells. A total of 79 phenothiazine derivatives were investigated to build the HQSAR model of power conversion efficiency. The fragment parameters of the established optimal HQSAR model are fragment size: “4 ∼ 7”, hologram length: “353”, and fragment distinction: “bonds, connections, chirality, donor and acceptor atoms”. The predictive performance of the HQSAR model was evaluated with external test set validation and leave-one-out cross-validation. The validation results demonstrate that the developed model possesses high prediction ability and stability. The molecular contribution map of this model was analyzed to reveal the key structures and modification positions for improving the power conversion efficiency, and was used to guide the molecular design of high performance dye sensitizers. The theoretical prediction result of several literature reported phenothiazine compounds indicates that the developed model is an effective and important guidance to the molecular design of dye sensitizers. Therefore, several new phenothiazine derivatives with theoretically improved power conversion efficiency were designed by the proposed HQSAR-based strategy.

Hologram QSAR and Topomer CoMFA Study on Naphthyridone Derivatives as ATAD2 Bromodomain Inhibitors

Background: ATAD2 is closely related to the occurrence and proliferation of many tumors. Thus, exploring ATAD2 inhibitors is greatly significant for the prevention and treatment of tumors. In this study, the quantitative structure–activity relationship (QSAR) analyses of 57 naphthyridone derivatives were conducted using hologram quantitative structure–activity relationship (HQSAR) and topomer comparative molecular field analysis (topomer CoMFA). Method: The 57 naphthyridone derivatives were divided into the training (44 derivatives) and testing (13 derivatives) sets. HQSAR and topomer CoMFA models were obtained by applying the SYBYL-X software and validated using various validation parameters. Contribution maps from the best HQSAR model and the contour maps from the best topomer CoMFA model were analyzed. Results: The most effective HQSAR model exhibited significant cross-validated (q2 = 0.872) and non cross-validated (r2 = 0.972) correlation coefficients, and the most effective topomer CoMFA model had q2 = 0.861 and r2 = 0.962. Several external validation parameters, such as , , , , and , were used to calculate the correlation coefficients of the test set samples and validate both models. The result exhibited a powerful predictive capability. Graphical results from HQSAR and topomer CoMFA were validated by the binding mode in the crystal structure. Conclusion: The models may be beneficial to enhance the understanding of the structure–activity relationships for this class of compounds and also provide useful clues for the design of potential ATAD2 bromodomain inhibitors.

In silico design of novel quinazoline-based compounds as potential Mycobacterium tuberculosis PknB inhibitors through 2D and 3D-QSAR, molecular dynamics simulations combined with pharmacokinetic predictions

Serine/threonine protein kinase B (PknB) is essential to Mycobacterium tuberculosis (M. tuberculosis) cell division and metabolism and a potential anti-tuberculosis drug target. Here we apply Hologram Quantitative Structure Activity Relationship (HQSAR) and three-dimensional QSAR (Comparative Molecular Similarity Indices Analysis (CoMSIA)) methods to investigate structural requirements for PknB inhibition by a series of previously described quinazoline derivatives. PknB binding of quinazolines was evaluated by molecular dynamics (MD) simulations of the catalytic domain and binding energies calculated by Molecular Mechanics/Poisson Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) methods. Evaluation of a training set against experimental data showed both HQSAR and CoMSIA models to reliably predict quinazoline binding to PknB, and identified the quinazoline core and overall hydrophobicity as the major contributors to affinity. Calculated binding energies also agreed with experiment, and MD simulations identified hydrogen bonds to Glu93 and Val95, and hydrophobic interactions with Gly18, Phe19, Gly20, Val25, Thr99 and Met155, as crucial to PknB binding. Based on these results, additional quinazolines were designed and evaluated in silico, with HQSAR and CoMSIA models identifying sixteen compounds, with predicted PknB binding superior to the template, whose activity spectra and physicochemical, pharmacokinetic, and anti-M. tuberculosis properties were assessed. Compound, D060, bearing additional ortho- and meta-methyl groups on its R2 substituent, was superior to template regarding PknB inhibition and % caseum fraction unbound, and equivalent in other aspects, although predictions identified hepatotoxicity as a likely issue with the quinazoline series. These data provide a structural basis for rational design of quinazoline derivatives with more potent PknB inhibitory activity as candidate anti-tuberculosis agents.

Virtual screening of PEBP1 inhibitors by combining 2D/3D-QSAR analysis, hologram QSAR, homology modeling, molecular docking analysis, and molecular dynamic simulations.

Human phosphatidylethanolamine binding protein 1 (hPEBP1) is a novel target affecting many cellular signaling pathways involved in the formation of metastases. It can be used in the treatment of many cases of cancer. For these reasons, pharmaceutical companies use computational approaches, including multi-QSAR (2D, 3D, and hologram QSAR) analysis, homology modeling, molecular docking analysis, and molecular dynamic simulations, to speed up the drug discovery process. In this paper, QSAR modeling was conducted using two quantum chemistry optimization methods (AM1 and DFT levels). As per PLS results, we found that the DFT/B3LYP method presents high predictability according to 2D-QSAR, CoMFA, CoMSIA, and hologram QSAR studies, with Q2 of 0.81, 0.67, 0.79, and 0.67, and external power with R2pred of 0.78, 0.58, 0.66, and 0.56, respectively. This result has been validated by CoMFA/CoMSIA graphics, which suggests that electrostatic fields combined with hydrogen bond donor/acceptor fields are beneficial to the antiproliferative activity. While the hologram QSAR models show the contributions of each fragment in improving the activity. The results from QSAR analyses revealed that ursolic acids with heterocyclic rings could improve the activities. Ramachandran plot validated the modeled PEBP1 protein. Molecular docking and MD simulations revealed that the hydrophobic and hydrogen bond interactions are dominant in the PEBP1's pocket. These results were used to predict in silico structures of three new compounds with potential anticancer activity. Similar molecular docking stability studies and molecular dynamics simulations were conducted.

Ensemble hologram quantitative structure activity relationship model of the chromatographic retention index of aldehydes and ketones

色谱保留指数(retention index, RI)是色谱分析中的重要参数,不同化合物在不同极性固定相上具有不同的保留行为。醛酮化合物种类众多,实验测定其RI值的时间和经济成本高。该论文采用集成建模(ensemble modeling)结合全息定量构效关系(HQSAR)方法研究了醛酮化合物在2种固定相(DB-210和HP-Innowax)上色谱保留指数的定量构效关系(QSAR)模型。用外部测试集验证法和留一交叉验证法评估了所建立模型的预测能力。首先建立了34种被研究化合物的个体HQSAR模型。在固定相DB-210上,片段特性(FD)为“供体/受体原子(DA)”且片段尺寸(FS)为1~9时可得到最优个体模型,在固定相HP-Innowax上,FD为“DA”且FS为4~7时可得到最优个体模型,这两个模型的交叉验证相关系数( )分别为0.935和0.909,外部验证相关系数( )分别为0.925和0.927,一致性相关系数(CCC)分别为0.953和0.960,预测平方相关系数F2( )分别为0.922和0.918,预测平方相关系数F3( )分别为0.931和0.927。研究结果表明醛酮化合物的分子结构与RI值之间存在定量关系,用HQSAR方法可以建立二者之间的QSAR模型。其次,以4个预测准确度最高的个体HQSAR模型作为子模型通过算术平均建立了集成HQSAR模型。建立的集成HQSAR模型预测被研究化合物在DB-210和HP-Innowax固定相上RI值的 分别为0.927和0.919, 分别为0.929和0.963, CCC分别为0.956和0.979, 分别为0.927和0.958, 分别为0.935和0.963。与个体HQSAR模型相比,建立的集成HQSAR模型预测准确度更高。这说明集成建模是提高HQSAR模型预测能力的有效方法,HQSAR与集成建模方法相结合可以用于研究和预测醛酮化合物的RI值。

Structure–activity relationship and molecular docking analysis of cysteine‐containing dipeptides as antioxidant and ACE inhibitory

SummaryComparative molecular field analysis (CoMFA), comparative similarity index analysis (CoMSIA) and hologram QSAR (HQSAR) were used to identify the relationship between the structure of cysteine‐containing dipeptide with antioxidant and ACE inhibitory activity; then, molecular docking, ADME prediction and thermostability assay were applied to illustrate the performance in docking with ACE, metabolic information and thermostability of the strongest activity dipeptide. According to pIC50 of 2, 2‐diphenyl‐1‐picrylhydrazyl (DPPH) and 2, 2′‐azinobis‐(3‐ethyl‐benzothiazoline‐6‐sulphonate) (ABTS), constructed CoMFA, CoMSIA and HQSAR models predicted the structural properties of carboxyl of C‐terminus impacted on the antioxidant activity. The properties of side chain of C‐terminus influenced on the ACE inhibitory activity based on CoMFA, CoMSIA and HQSAR models. As the strongest multifunctional cysteine‐containing dipeptide, CW interacted with ACE by S1 pocket, S2 pocket, Zn2+. It was promising for CW to utilise in the functional foods due to less interference in normal life activities, high oral bioavailability and stability under 70 °C.

3D-QSAR, HQSAR, molecular docking, and new compound design study of 1,3,6-trisubstituted 1,4-diazepan-7-ones as human KLK7 inhibitors

The skin is an important barrier against environmental factors. Foregoing studies has shown that human KLK7 is a protease which promoted epidermal shedding, affected the epidermal barrier and increased the risk of atopic dermatitis. In this study, the structure and activity relationship of 40 human KLK7 inhibitors was explored by three-dimensional quantitative structure–activity relationship (3D-QSAR) and hologram quantitative structure–activity relationship (HQSAR). 3D-QSAR including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis fields (CoMSIA), and Topomer comparative molecular field analysis (Topomer CoMFA). From the data we got, the 3D-QSAR models (CoMFA with q2 = 0.755, r2 = 0.994; CoMSIA with q2 = 0.602, r2 = 0.980; Topomer CoMFA with q2 = 0.644, r2 = 0.929) and the HQSAR model (q2 = 0.717, r2 = 0.950) had a good predictability. Molecular docking was used to reveal the binding mode between the inhibitors and KLK7 protein further. 3D-QSAR, HQSAR, and molecular docking results also provided guidance for discovering new human KLK7 inhibitors. Finally, 13 new compounds were designed as potential human KLK7 inhibitors, and the predicted activity values showed an effective inhibition on human KLK7.

Hologram QSAR study on the critical micelle concentration of Gemini surfactants

A quantitative structure-property relationship (QSPR) study on the critical micelle concentration (CMC) of 120 Gemini surfactants was performed with hologram quantitative structure-activity relationship (HQSAR) technique. In the proposed HQSAR model, three fragment parameters, fragment distinction, fragment size and fragment length, were set to “A, B, C and D”, “7–10” and “307” respectively. Two conventional validation techniques, external test set validation and leave-one-out cross-validation (LOO-CV), were utilized to evaluate the forecasting accuracy of as-proposed model. As the result, the QF32, concordance correlation coefficient, root mean squared error, r¯m2 and Δr m2 of external test set validation is 0.9799, 0.9803, 0.1757, 0.9429 and 0.0100, respectively. The root mean squared error, r¯m2 and Δr m2 of LOO-CV is 0.2734, 0.9296 and 0.0436, respectively. It is demonstrated that the HQSAR model built in this work is available and accurate for preliminarily studying and predicting the CMC of Gemini surfactants. Moreover, HQSAR is shown to be a promising approach for building up QSPR model in relation to the CMC of Gemini surfactants.

Biological enrichment prediction of polychlorinated biphenyls and novel molecular design based on 3D-QSAR/HQSAR associated with molecule docking.

Based on the experimental data of octanol-water partition coefficients (Kow, represents bioaccumulation) for 13 polychlorinated biphenyl (PCB) congeners, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to establish 3D-QSAR models, combined with the hologram quantitative structure–activity relationship (HQSAR), the substitution sites (mono-substituted and bis-substituted) and substituent groups (electron-withdrawing hydrophobic groups) that significantly affect the octanol-water partition coefficients values of PCBs were identified, a total of 63 monosubstituted and bis-substituted were identified. Compared with using 3D-QSAR model alone, the coupling of 3D-QSAR and HQSAR models greatly increased the number of newly designed bis-substituted molecules, and the logKow reduction in newly designed bis-substituted molecules was larger than that of monosubstituted molecules. This was established to predict the Kow values of 196 additional PCBs and carry out a modification of target molecular PCB-207 to lower its Kow (biological enrichment) significantly, simultaneously maintaining the flame retardancy and insulativity after calculation by using Gaussian09. Simultaneously, molecular docking could further screen out three more environmental friendly low biological enrichment newly designed PCB-207 molecules (5-methyl-PCB-207, 5-amino-PCB-207, and 4-amino-5-ethyl-PCB-207).

HQSAR and random forest-based QSAR models for anti-T. vaginalis activities of nitroimidazoles derivatives

Trichomonas vaginalis is the causative agent of trichomoniasis, a highly prevalent sexually transmitted infection worldwide. Nitroimidazole drugs, such as metronidazole and tinidazole, are the only recommended treatment, but cases of resistance represent at least 5%. In case of resistance or therapeutic failure, posology with higher doses is used, culminating in the increase of the toxic effects of the treatment. In this context, the development of new drugs becomes an eminent necessity. Hologram quantitative structure-activity relationship (HQSAR) models using nitroimidazole derivatives were generated to discover the relationship between the different chemical structures and the activity against cells and the selectivity against susceptible and resistant strains. One model of each strain was chosen for interpretation, both showed good internal coefficient (q2LOO values: 0.607 for susceptible strain and 0.646 for resistant strain subsets) and great values in other internal and external validations metrics. From the contribution of fragments to HQSAR models, several differences between the most and least potent compounds were found: 5-nitroimidazole contributes positively while 4-nitroimidazole negatively. QSAR models employing random forest (RF-QSAR) machine learning technique were also built and a robust model was obtained from resistant strain activity prediction (q2LOO equals to 0.618). The constructed HQSAR and RF-QSAR models were employed to predict the activity of three newly planned nitroimidazole derivatives in the design of new drugs candidates against T. vaginalis strains.

Design and prediction of favorable substitution site in trifluorophenyl-substituted homopiperazine, pyrazoline, triazepane derivatives as dipeptidyl peptidase IV Inhibitors: HQSAR and docking studies

In this study, hologram quantitative structure–activity relationship (HQSAR) and molecular docking studies were performed on a dataset of 108 trifluorophenyl homopiperazine, pyrazoline, and triazepane derivatives as dipeptidyl peptidase IV inhibitors. HQSAR model was obtained using atoms, connection, donor, and acceptor as fragment distinction parameters with fragment size (4–7) using components (q2 = 0.738, r2 = 0.962). Molecular docking study was performed to identify novel potent inhibitors and the important amino acid residues, which formed an interaction with compound 105, were Ser-631, His-741, Tyr-663, Glu-204, Arg-123 and Ala-655 with receptor. These models were used to design new compounds for homopiperazine, pyrazoline, triazepane analogs and the results obtained from this study could be useful for further investigations.

Comparative molecular field analysis and hologram quantitative structure activity relationship studies of pyrimidine series as potent phosphodiesterase 10A inhibitors

The inhibition of phosphodiesterase (PDE) 10A enzyme as an alternative approach is shown to have potential in the current treatment of schizophrenia. In the present work, the inhibition activity of 87 structurally diverse pyrimidine derivatives as PDE10A inhibitors were subjected to 2‐dimensional quantitative structure activity relationship (2D‐QSAR) and 3D‐QSAR studies using hologram QSAR and comparative molecular field analysis (CoMFA), respectively. The best CoMFA model gave a noncross‐validated correlation coefficient, () = 0.977, SEtrain = 0.247 and predictive correlation coefficient () = 0.957, and SEtest = 0.358. For the hologram QSAR (HQSAR) model, the similar parameters were , SEtrain = 0.565, , and SEtest = 0.872. The leave‐one‐out (LOO) cross‐validated r2 (q2) were 0.78 and 0.8 for the CoMFA and HQSAR models, respectively. These statistics indicate that the CoMFA model is more satisfactory than the HQSAR. Analysis of CoMFA steric and electrostatic contour maps strongly demonstrate that the presence of bulky group at the end of R1 substituent, especially heterocyclic aromatic rings, increase the inhibitory activities, and electronegative groups, such as alkoxide at the R2 position, enhance the inhibitory activities of studied chemicals. The results of this study serves as a guide for the possible modifications and designing of novel pyrimidine derivatives as new potent PDE10A inhibitors.

Combined topomer CoMFA and hologram QSAR studies of a series of pyrrole derivatives as potential HIV fusion inhibitors

To have a better understanding of the relationship between structures and inhibitory activities of HIV fusion inhibitors, topomer comparative molecular field analysis (Topomer CoMFA) and hologram quantitative structure–activity relationship (HQSAR) were applied to study a series of pyrrole derivatives as potential HIV fusion inhibitors. Statistical results from the Topomer CoMFA model showed believable predictability based on the non-cross-validated value (r2 = 0.96), and the cross-validated value (q2 = 0.63). The r2 and q2 from the HQSAR model were 0.96 and 0.66, respectively, which also indicated the excellent predictability. External validations further proved the predictive potent of these two models with the $$r_{pred}^2$$ values of 0.96 for both the two models. Statistical results of the two models showed that they are all credible. In addition, we found a phenomenon that for some compound, bulky and negatively charged substitutions were favourable to its activity in the Topomer COMFA, but nitrogen contributed more positively than the bulkier and more negative chorine in the HQSAR. Therefore, in order to obtain more potential hits, the two models should be combined together. These studies would provide insights for the structure–activity relationship understanding of the small-molecule fusion inhibitors and be helpful for the rational design of novel fusion inhibitors in the future.

An explorative study on potent Gram-negative specific LpxC inhibitors: CoMFA, CoMSIA, HQSAR and molecular docking

Pathogenic Gram-negative bacteria are responsible for nearly half of the serious human infections. Hologram quantitative structure–activity relationships (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) were implemented on a group of 32 of potent Gram-negative LpxC inhibitors. The most effective HQSAR model was obtained using atoms, bonds, donor, and acceptor as fragment distinction. The cross-validated correlation coefficient (q2), non-cross-validated correlation coefficient (r2), and predictive correlation coefficient (r2Pred) for test set of HQSAR model were 0.937, 0.993, and 0.892, respectively. The generated models were found to be statistically significant as the CoMFA model had (r2 = 0.967, q2 = 0.804, r2Pred = 0.827); the CoMSIA model had (r2 = 0.963, q2 = 0.752, r2Pred = 0.857). Molecular docking was employed to validate the results of the HQSAR, CoMFA, and CoMSIA models. Based on the obtained information, six new LpxC inhibitors have been designed.

Identification of molecular fingerprints of phenylindole derivatives as cytotoxic agents: a multi-QSAR approach

Phenylindole is reported to be an interesting scaffold having promising cytotoxic activities and can overcome the cancer drug resistance possibly via binding to the colchicine binding site of tubulin. In order to find out the molecular fingerprints for the better cytotoxic activity of phenylindole derivatives, multiple validated chemometric modeling approaches namely hologram QSAR (HQSAR), Bayesian classification model, and pharmacophore mapping analyses were applied into a dataset of 102 phenylindole derivatives. The final HQSAR model shows good statistical significance (Q2 = 0.760; R2Train = 0.868; R2Test = 0.660), and the best pharmacophore hypothesis has the highest regression coefficient value (r = 0.975) and the lowest RMS value of 0.679. Moreover, the Bayesian model is also statistically validated and robust to discriminate the cytotoxic and non-cytotoxic phenylindoles. These studies suggest that the amine group should be unsubstituted for retaining higher cytotoxicity. The pharmacophore mapping and Bayesian classification study suggest the importance of 2-phenyl group as a ring aromatic feature conducive to cytotoxicity. The steric and hydrophobic effect of long chain linear alkyl group has a positive influence on cytotoxicity as evidenced by the multi-QSAR study. Therefore, this multi-QSAR modeling reported here is beneficial in designing potential phenylindole cytotoxic agents in future.

Hologram quantitative structure–activity relationship and topomer comparative molecular-field analysis to predict the affinities of azo dyes for cellulose fibers

A hologram quantitative structure–activity relationship (HQSAR) and topomer comparative molecular-field analysis (CoMFA) were performed on 51 molecules that belong to the azo dyes to determine their affinities to cellulose fiber. The best HQSAR model was obtained by using atoms. Hydrogen atoms as a fragment distinction and a fragment size of 3–6 showed a leave-one-out cross-validated correlation coefficient (q2LOO) of 0.849 and a non-cross-validated correlation coefficient (r2) of 0.927. The best topomer CoMFA model with steric and electrostatic field parameters based on three fragments gave satisfactory results (q2LOO = 0.821; r2 = 0.934). External predictions were made on a test set and compared with previously reported models. Topomer CoMFA steric, electrostatic, and HQSAR atomic contribution maps were generated to analyze the structural features of this data set that govern their affinity potency.

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.