Quantitative structure-activity relationships for a series of selective estrogen receptor-beta modulators

The estrogen receptor-beta subtype (ERβ) is an attractive drug target for the development of novel therapeutic agents for hormone replacement therapy. Hologram quantitative structure-activity relationships (HQSAR) were conducted on a series of 6-phenylnaphthalene and 2-phenylquinoline derivatives, employing values of ERβ binding affinity. A training set of 65 compounds served to derive the models. The best statistical HQSAR model (q ²?=?0.73 and r ²?=?0.91) was generated using atoms, bonds, connections and donor and acceptor as fragment distinction parameters, and fragment size default (4–7) with hologram length of 199. The model was used to predict the binding affinity of an external test set of 16 compounds, and the predicted values were in good agreement with the experimental results. The final HQSAR model and the information obtained from 2D contribution maps should be useful for the design of novel ERβ modulators having improved affinity.     

3-吡啶基醚类化合物的分子全息QSAR研究

采用分子全息定量构效关系(HQSAR, hologram quantitative structure-activity relationship)方法, 研究了28个3-吡啶基醚类化合物对乙酰胆碱α4β2受体的亲和性与它们的分子结构之间的关系, 讨论了分子碎片大小、分子碎片亚结构类型以及分子全息长度对QSAR的影响, 得到了较好的HQSAR模型, 模型的交叉验证系数平方q²＝0.670, 非交叉相关系数平方r²＝0.965, 偏差S＝0.093. 利用HQSAR的颜色编码, 对化合物中不同基团对亲和活性的影响进行了讨论, 对新配体的合成具有一定的指导作用.     

A comparative study of quantitative structure-activity relationship methods based on gallic acid derivatives

By using hologram quantitative structure-activity relationship (HQSAR) and comparative molecular field analysis (CoMFA) methods, the relationships between the structures of 49 gallic acid derivatives and their analgesic activity have been investigated to yield statistically reliable models with considerable predictive power. The best HQSAR model was generated using atoms, bond and connectivity as fragment distinction parameters and fragment size 5-7 from a hologram length of 307 with 3 components. High conventional r2 (r2 = 0.825) and cross-validation r2 (r2(cv) = 0.726) values were obtained. CoMFA analyses varying lattice size and location, grid spacing, probe charges and using, Tripos standard and Indicator force field were performed. The best model was developed with 4 components using sp3-hybridized carbon atom with +1.0 charge as probe, grid spacing (2 A), lattice offset (1.0, 3.0, -2.5). The CoMFA model showed a conventional correlation coefficient r2 of 0.889 and across-validation r2(cv) equals to 0.633. The robustness and predictive ability of the HQSAR and CoMFA models have been validated by means of an external test set. The results indicate that both models possess high statistical quality in the prediction of analgesic potency of novel gallic acid analogs.     

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

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

三苯胺类染料敏化太阳能电池能量转化效率的全息定量构效关系研究

研究构建了三苯胺类化合物分子结构与相应染料敏化太阳能电池能量转化效率(PCE)之间的全息定量构效关系(HQSAR)模型。当fragment distinction、fragment size、hologram length和principal components分别为“C、DA”、“4-7”、“199”和“6”时,可以获得最优HQSAR模型。采用外部测试集验证和留一交叉验证对所建立模型进行检验,外部测试集验证中CCC和 Q2F3分别为0.933和0.892,留一交叉验证中其q2cv和r2分别为0.791和0.902,表明所建立模型具有较好的拟合效果和预测能力。通过所建立HQSAR模型的分子贡献图可知,环戊并二噻吩基团的存在有利于提高PCE值,长烷基链的存在可能降低PCE值。     

苯并咪唑类缓蚀剂的HQSAR研究及分子设计

采用分子全息定量构效关系(HQSAR)方法, 构建苯并咪唑衍生物在酸性环境中的缓蚀性能与结构之间的定量构效关系模型, 研究不同碎片区分参数及碎片大小对模型质量的影响, 寻找最优HQSAR模型, 并对其稳定性及预测能力进行评价. 结果显示: 选取碎片区分参数为原子类型(A)、化学键类型(B)、连接性(C)、氢原子(H)、手性(Ch)、氢键给体和受体(D&;A), 碎片大小为1-3 建模时, 得到的HQSAR 模型(r²(非交叉验证系数)=0.996, q²(交叉验证系数)=0.960, SE_cv(交叉验证标准误差)=3.709)具有良好的统计学稳定性及预测能力. 根据最优HQSAR模型图设计出的38种苯并咪唑类化合物理论上均具有较好的缓蚀性能. 本研究为油气田新型高效缓蚀剂研发提供可靠的理论依据.     

Synthesis of novel nitro-substituted triaryl pyrazole derivatives as potential estrogen receptor ligands

Novel tetrasubstituted pyrazole derivatives bearing a nitro substituent on their A-phenol ring were synthesized and their binding affinity towards the estrogen receptor (ER) subtypes ERalpha and ERbeta was determined. Among compounds tested, the 2-nitrophenol derivative 5c was found to bind satisfactorily to both estrogen receptor subtypes (RBAalpha=5.17 and RBAbeta=3.27). In general, the introduction of a nitro group into the A ring of these compounds was found to benefit their ERbeta binding abilities.     

CoMFA, CoMSIA, and molecular hologram QSAR studies of novel neuronal nAChRs ligands-open ring analogues of 3-pyridyl ether

3-Pyridyl ethers are excellent nAChRs ligands, which show high subtype selectivity and binding affinity to alpha4beta2 nAChR. Although the quantitative structure-activity relationship (QSAR) of nAChRs ligands has been widely investigated using various classes of compounds, the open ring analogues of 3-pyridyl ethers have been less involved in these studies due to the greater flexibility of this kind of molecule. In this study, two three-dimensional QSAR techniques and one two-dimensional QSAR technique were used to correlate the molecular structure with the biological activity of 64 analogues of 3-pyridyl ethers. Three different QSAR models were established. Their performances in the QSAR studies of open ring analogues of 3-pyridyl ethers were evaluated by the statistical values in the corresponding models. All models exhibited satisfactory predictive power. Of these models, the HQSAR behaved optimally in terms of the statistical values with q2=0.845, r2=0.897. Finally, graphic interpretation of three different models provided coincident information about the interaction of the ligand-receptor complex and supplied useful guidelines for the synthesis of novel, potent ligands.     

Drug discovery studies on quinoline-based derivatives as potential antimalarial agents

Molecular modelling studies were performed to identify the essential structural requirements of quinoline-based derivatives for improving their antimalarial activity. The developed CoMFA, CoMSIA and HQSAR models for a training set comprising 37 derivatives showed good statistical significance in terms of internal cross validation (q²) 0.70, 0.69 and 0.80 and non-cross validation (r²) 0.80, 0.79 and 0.80. Also, the predicted r² values (r²_pred) of 0.63, 0.61 and 0.72 for a test set consisting of 12 compounds suggested significant predicting ability of the models. Structural features were correlated in terms of steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor interactions. Furthermore, the bioactive conformation was explored and explained by docking compounds #28, 32 and 40 into the active binding site of lactate dehydrogenase of Plasmodium falciparum. The QSAR models, contour map and docking binding affinity obtained could be successfully utilized as a guiding tool for the design and discovery of novel quinoline-based derivatives with potent antimalarial activity.     

Predicting toxicity of benzene derivatives by molecular hologram derived quantitative structure-activity relationships (QSARS)

Holographic quantitative structure-activity relationship (HQSAR) is an emerging QSAR technique with the combined application of molecular hologram, which encodes the frequency of occurrence of various molecular fragment types, and the subsequent partial least squares (PLS) regression analysis. Based on molecular hologram, alignment-free QSAR models could be rapidly and easily developed with highly statistical significance and predictive ability. In this paper, the toxicity data for a series of 83 benzene derivatives to the autotrophic Chlorella vulgaris (IGC50, negative logarithmic form of 6-h 50% population growth inhibition concentration in mmol/l) were subjected to HQSAR analysis and this resulted in a model with a high predictive ability. The robustness and predictive ability of the model were validated by "leave-one-out" (LOO) cross-validation procedure and an external testing set. The influence of fragment distinction parameters and fragment size on the quality of the HQSAR model have been also discussed.     

Predicting toxicity of benzene derivatives by molecular hologram derived quantitative structure–activity relationships (QSARS)

Holographic quantitative structure–activity relationship (HQSAR) is an emerging QSAR technique with the combined application of molecular hologram, which encodes the frequency of occurrence of various molecular fragment types, and the subsequent partial least squares (PLS) regression analysis. Based on molecular hologram, alignment-free QSAR models could be rapidly and easily developed with highly statistical significance and predictive ability. In this paper, the toxicity data for a series of 83 benzene derivatives to the autotrophic Chlorella vulgaris (IGC⁵⁰, negative logarithmic form of 6-h 50% population growth inhibition concentration in mmol/l) were subjected to HQSAR analysis and this resulted in a model with a high predictive ability. The robustness and predictive ability of the model were validated by “leave-one-out” (LOO) cross-validation procedure and an external testing set. The influence of fragment distinction parameters and fragment size on the quality of the HQSAR model have been also discussed.     

QSAR analyses on avian influenza virus neuraminidase inhibitors using CoMFA, CoMSIA, and HQSAR

The recent wide spreading of the H5N1 avian influenza virus (AIV) in Asia, Europe and Africa and its ability to cause fatal infections in human has raised serious concerns about a pending global flu pandemic. Neuraminidase (NA) inhibitors are currently the only option for treatment or prophylaxis in humans infected with this strain. However, drugs currently on the market often meet with rapidly emerging resistant mutants and only have limited application as inadequate supply of synthetic material. To dig out helpful information for designing potent inhibitors with novel structures against the NA, we used automated docking, CoMFA, CoMSIA, and HQSAR methods to investigate the quantitative structure-activity relationship for 126 NA inhibitors (NIs) with great structural diversities and wide range of bioactivities against influenza A virus. Based on the binding conformations discovered via molecular docking into the crystal structure of NA, CoMFA and CoMSIA models were successfully built with the cross-validated q (2) of 0.813 and 0.771, respectively. HQSAR was also carried out as a complementary study in that HQSAR technique does not require 3D information of these compounds and could provide a detailed molecular fragment contribution to the inhibitory activity. These models also show clearly how steric, electrostatic, hydrophobicity, and individual fragments affect the potency of NA inhibitors. In addition, CoMFA and CoMSIA field distributions are found to be in well agreement with the structural characteristics of the corresponding binding sites. Therefore, the final 3D-QSAR models and the information of the inhibitor-enzyme interaction should be useful in developing novel potent NA inhibitors.     

醛酮化合物色谱保留指数的集成全息定量构效关系模型

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H...     

QSAR analyses of 3-(4-benzylpiperidin-1-yl)-N-phenylpropylamine derivatives as potent CCR5 antagonists

CCR5 receptor binding affinity of a series of 3-(4-benzylpiperidin-1-yl)propylamine congeners was subjected to QSAR study using the linear free energy related (LFER) model of Hansch. Appropriate indicator variables encoding different group contributions and different physicochemical variables such as hydrophobicity (pi), electronic (Hammett sigma), and steric (molar refractivity, STERIMOL values) parameters of phenyl ring substituents of the compounds were used as predictor variables. The Hansch analysis explores the importance of the lipophilicity and electron-donating substituents for the binding affinity. However, this method could not give more insight into the structure-activity relationships because of the diverse molecular features in the data set. 3D-QSAR analyses of the same data set using Molecular Shape Analysis (MSA), Receptor Surface Analysis (RSA), and Molecular Field Analysis (MFA) techniques were also performed. The best model with acceptable statistical quality was derived from the MSA, which showed the importance of the relative negative charge (RNCG): substituents with a high RNCG value have more binding affinity than the unsubstituted piperidine and phenyl (R1 position) congeners. The relative negative charge surface area (RNCS) is detrimental (e.g. R2 = 3,4-Cl2) for the activity. An increase in the length of the molecule in the Z dimension (Lz) is conducive (e.g. R3 = sulfonylmorpholino), while an increase in the area of the molecular shadow in the XZ plane (Sxz) is detrimental (e.g. R1 = N-c-hexylmethyl-5-oxopyrrolidin-3-yl) for the binding affinity. The presence of a chiral center makes the molecule less active (e.g. R1 = N-methyl-5-oxopyrrolidin-3-yl). An increase in the van der Waals area, the molecular volume, and the difference between the volume of the individual molecule and the shape reference compound are conducive (e.g. R3 = (CH3)2NSO2-) for the binding affinity. Substituents with higher JursFPSA_2 values (fractional charged partial surface area) like the N-methylsulfonylpiperidin-4-yl (R1 position) group have better binding affinity than the substituents such as 4-chlorophenylamino (R1 position). Unsubstituted piperidines (R1 position) with less JursFNSA_1 values have lower binding affinity than the 4-chlorophenyl substituted compounds. The MFA derived equation shows interaction energies at different grid points, while the RSA model shows the importance of hydrophobicity and charge at different regions of the molecules. The models were validated through the leave-one-out, leave-15%-out, and leave-25%-out cross-validation techniques. The developed models were also subjected to a randomization test (99% confidence level). Although the MSA derived models had excellent statistical qualities both for the training as well as test sets, RSA and MFA results for the test sets are not comparable statistically with the MSA derived models.     

Developing an antituberculosis compounds database and data mining in the search of a motif responsible for the activity of a diverse class of antituberculosis agents

A novel data mining procedure to look for new antitubercular agents and targets as well as to find a minimum common bioactive substructure (MCBS), has been reported here. The methodology extracts MCBS, both across the diverse chemical classes and within the particular chemical class, known to be present in the various marketed drugs alongside antimycobacterial compounds with known MICs. For this purpose a small in-house database of compounds has been created, for which MICs against Mycobacterium are known. The compounds have been collected from literature available on the synthetic compounds, having known MICs against Mycobacterium tuberculosis. An elaborate HQSAR (Hologram QSAR) study has been attempted to extract active fragment from a diverse class of compounds, in combination with the clustering technique to select a homogeneous group of compounds having good a profile toward the activity. The 2D pharmacophore (the 2D fragments extracted from HQSAR) has been validated searching the database. It has been found further that this validated 2D pharmacophore could be used for searching the orphan target in Mycobacterium effectively.     

Fingerprint directed scaffold hopping for identification of CCR2 antagonists

Chemokine receptors have evolved as attractive targets for disease conditions which arise due to immunomodulation involving host-defense mechanisms. CCR2, a chemokine receptor, is targeted for diseases like arthritis, multiple sclerosis, vascular disease, obesity, and type 2 diabetes. This study provides a new strategy of a ligand based technique which exploits fingerprint led fragment features in conjunction with structure-guided design for identifying new scaffolds for CCR2. A fragment based mining (FBM) technique was employed on a chemical database to identify novel scaffold hops. The hits were subjected to 3-point pharmacophore fingerprint procedures with Tanimoto similarity metric to compare pharmacophoric fingerprints. The final 66 hits generated by these exercises were predicted by the validated HQSAR model, and the top predicted were suggested as probable scaffolds for CCR2 antagonism. The identified scaffolds were validated through molecular docking studies. The ligands were docked by providing receptor flexibility in the extra cellular domain (1 and 3), N terminal domain, and in the transmembrane (TM1 & TM7) helix region with IFD approach. Some of the scaffolds showed H-bonding potential which was not explored by the data set molecules. All identified scaffolds highlighted a key hydrogen bonding interaction with Thr292 as supported by mutational studies. The observed pi stacking interaction with Tyr188 in data set molecules was also produced by the new scaffolds. Taking the advantage of receptor flexibility the scaffolds explored the hydrophobic binding cleft between helix 1 and 7 occupied by residues Leu44, Leu45, Leu48 and Ile300, Ile303, Ile304, respectively. Two of the identified molecules have promising outcomes and can be considered as novel scaffolds for CCR2 binding.     

5,6-二氢-(9H)-吡唑[3,4-c]-1,2,4-三唑[4,3-a]吡啶类抑制剂的HQSAR研究

应用分子全息定量构效关系(HQSAR)分析方法,以5,6-二氢-(9H)-吡唑[3,4-c]-1,2,4-三唑[4,3-a]吡啶类抑制剂为研究对象,建立了一组对磷酸二酯酶4有抑制活性的化合物HQSAR模型,分析化合物活性与分子结构之间的关系.探讨了分子全息长度、分子碎片大小以及碎片区分参数对模型质量的影响.最优模型的交叉验证相关系数q2=0.628,非交叉验证相关系数r2=0.930,标准偏差SE=0.277.该模型具有较好的预测能力,对该类化合物性质的预测及进一步合成工作有指导意义.