Fragment-similarity-based QSAR (FS-QSAR) algorithm for ligand biological activity predictions

Quantitative structure–activity relationship (QSAR) studies are useful computational tools often used in drug discovery research and in many scientific disciplines. In this study, a robust fragment-similarity-based QSAR (FS-QSAR) algorithm was developed to correlate structures with biological activities by integrating fragment-based drug design concept and a multiple linear regression method. Similarity between any pair of training and testing fragments was determined by calculating the difference of lowest or highest eigenvalues of the chemistry space BCUT matrices of corresponding fragments. In addition to the BCUT-similarity function, molecular fingerprint Tanimoto coefficient (Tc) similarity function was also used as an alternative for comparison. For validation studies, the FS-QSAR algorithm was applied to several case studies, including a dataset of COX2 inhibitors and a dataset of cannabinoid CB2 triaryl bis-sulfone antagonist analogues, to build predictive models achieving average coefficient of determination (r ²) of 0.62 and 0.68, respectively. The developed FS-QSAR method is proved to give more accurate predictions than the traditional and one-nearest-neighbour QSAR methods and can be a useful tool in the fragment-based drug discovery for ligand activity prediction.     

Synthesis,antimicrobial activity and QSAR studies of 2,5-disubstituted benzoxazoles

In this study, a new series of 2,5-disubstituted benzoxazoles was synthesized and their structures were elucidated by elemental analysis, MASS, ¹H-NMR, ¹³C-NMR and IR spectral data. Newly and previously synthesized 2,5-disubstituted benzoxazole derivatives were evaluated for antibacterial and antifungal activity against standard strains and their drug-resistant isolates. Microbiological results showed that the compounds presented a large spectrum of activity having MIC values of 250–7.8 µg mL^?1 against the tested microorganisms. Among the newly synthesized derivatives 3–22, compound 11 was the most active against Candida krusei out of all; however, it was one dilution less potent than standard drug fluconazole. In addition, all the new and previous compounds were more active than standard drugs ampicillin trihydrate and rifampicin against Pseudomonas aeruginosa and its gentamicin-resistant isolate. The 2D-QSAR (Quantitative Structure–Activity Relationship) analysis of a set of newly and previously synthesized benzoxazoles tested for growth inhibitory activity against methicillin-resistant Staphylococcus aureus (MRSA) was also performed by using multivariable regression analysis. The activity contributions for substituent effects of these compounds were determined from the correlation equation for predictions of the lead optimization.     

A theoretical study of angiotensin-converting enzyme inhibitors

Summary INDO molecular orbital calculations are reported for 35 selected angiotensin-converting enzyme inhibitors. QSARs are developed between pI₅₀ data and molecular electronic indices. The QSARs obtained reflect the importance of both charge-charge interactions between inhibitor and receptor and of specific interactions between groups on the inhibitor with points around the molecule which are postulated to correspond to binding sites at the receptor.     

QSAR study and VolSurf characterization of anti-HIV quinolone library

Antiviral quinolones are promising compounds in the search for new therapeutically effective agents for the treatment of AIDS. To rationalize the SAR for this new interesting class of anti-HIV derivatives, we performed a 3D-QSAR study on a library of 101 6-fluoro and 6-desfluoroquinolones, taken either from the literature or synthesized by us. The chemometric procedure involved a fully semiempirical minimization of the molecular structures by the AMSOL program, which takes into account the solvatation effect, and their 3D characterization by the VolSurf/GRID program. The QSAR analysis, based on PCA and PLS methods, shows the key structural features responsible for the antiviral activity.     

A QSAR study and molecular design of benzothiazole derivatives as potent anticancer agents

A quantitative structure-activity relationship (QSAR) of a series of benzothiazole derivatives showing a potent and selective cytotoxicity against a tumorigenic cell line has been studied by using the density functional theory (DFT), molecular mechanics (MM ) and statistical methods, and the QSAR equation was established via a correlation analysis and a stepwise regression analysis. A new scheme determining outliers by "leave-one-out" (LOO) cross-validation coefficient (q2n-i) was suggested and successfully used. In the established optimal equation (excluding two outliers), the steric parameter (MRR) and the net charge (QFR) of the first atom of the substituent (R), as well as the square of hydrophobic parameter (lgP)2 of the whole molecule, are the main independent factors contributing to the anticancer activities of the compounds. The fitting correlation coefficient (R2) and the cross-validation coefficient (q2) values are 0.883 and 0.797, respectively. It indicates that this model has a significantly statistical quality and an excellent prediction ability. Based on the QSAR studies, 4 new compounds with high predicted anticancer activities have been theoretically designed and they are expected to be confirmed experimentally.     

一种新的氨基酸描述符及其在肽QSAR中的应用

从20种天然氨基酸的41个randic molecular profiles非零描述符、44个eigenvalue based indices非零描述符和47个walk and path counts非零描述符分别进行主成分分析,得出一种新的氨基酸描述符-SVREW。将其应用于血管紧张素转化酶(ACE)抑制二肽和ACE抑制三肽、苦味二肽和苦味四肽、后叶催产素类似物、HLA-A*0201限制性CTL表位肽的结构表征,应用多元线性回归(MLR)建立定量构效关系模型,同时采用内部与外部双重验证的方法验证模型的稳定性。所建ACE抑制二肽、ACE抑制三肽、苦味二肽、苦味四肽、后叶催产素类似物、HLA-A*0201限制性CTL表位肽的模型复相关系数(R2cum)分别为0.994,0.797,0.948,0.878,0.686,0.720;留一法交互校验复相关系数(R2cv)分别为0.955,0.859,0.879,0.958,0.796,0.843;外部样本校验相关系数(Q2ext)分别为0.990,0.954,0.890,0.950,0.748,0.773。经研究表明SVREW描述符用于肽分子结构表征所建模型的稳定性与预测能力均较好,有望成为多肽定量构效关系研究中一种有效的结构表征方法,可对新药物的发现和研究提供指导。     

A new strategy to improve the predictive ability of the local lazy regression and its application to the QSAR study of melanin‐concentrating hormone receptor 1 antagonists

In the quantitative structure‐activity relationship (QSAR) study, local lazy regression (LLR) can predict the activity of a query molecule by using the information of its local neighborhood without need to produce QSAR models a priori. When a prediction is required for a query compound, a set of local models including different number of nearest neighbors are identified. The leave‐one‐out cross‐validation (LOO‐CV) procedure is usually used to assess the prediction ability of each model, and the model giving the lowest LOO‐CV error or highest LOO‐CV correlation coefficient is chosen as the best model. However, it has been proved that the good statistical value from LOO cross‐validation appears to be the necessary, but not the sufficient condition for the model to have a high predictive power. In this work, a new strategy is proposed to improve the predictive ability of LLR models and to access the accuracy of a query prediction. The bandwidth of k neighbor value for LLR is optimized by considering the predictive ability of local models using an external validation set. This approach was applied to the QSAR study of a series of thienopyrimidinone antagonists of melanin‐concentrating hormone receptor 1. The obtained results from the new strategy shows evident improvement compared with the commonly used LOO‐CV LLR methods and the traditional global linear model. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

A QSAR of the toxicity of amino-benzenes and their structures

The quantum chemical parameters and the topological indices have been calculated for the prediction of the toxicity of amino-benzenes in the environment, and work has been done on the multiple regression and neural networks. The combination of CoMFA with formation heat yields greatly improved results. A good model has been obtained which provides a basis for the studies of the toxic action mechanism.     

Modelling of the Biological Activity for a Set of Ceramic Fibre Materials: A QSAR Study

Abstract

The objective was to develop quantitative structure-activity relationships (QSARs) for a set of nine ceramic raw materials. The samples were characterized by a chemical analysis (both X-ray fluorescence and neutron activation analysis) and the morphology was determined by electron microscopy in combination with automated image analysis. Further, the fibre samples were subjected to two biological activity assays, measuring cytotoxicity and hydroxyl radical production. To investigate the produced data structures, principal component analysis (PCA) and partial least squares (PLS) were applied together with rigorous validation techniques. Significant QSARs were found for both biological activity assays. The morphology of the fibres plays an important role for the cytotoxicity and their trace element background is related to the hydroxyl radical production.     

Antibacterial activity of extract and fractions from branches of Protium spruceanum and cytotoxicity on fibroblasts

The crude ethanol extract (CEE) and fractions from branches of Protium spruceanum were subjected to antibacterial and cytotoxicity assays. Compounds of the most active fraction were identified by GC-MS and LC-MS. CEE was active against 19 bacteria and the ethyl acetate fraction (EAF) showed the lowest minimum bactericidal concentration (MBC 0.3–80.0 mg/mL). Through time-kill assay was observed that EAF induced rapid bactericidal effect against Staphylococcus saprophyticus. The cytotoxicity tests against L929 fibroblasts showed great potential of EAF on the treatment of infections caused by five bacteria (MBC < IC₅₀). The results provide in vitro scientific support to the possible application of branches of P. spruceanum as antimicrobial agent that may contribute for treatment of infections.