e-Statistics for deriving QSAR models

This paper presents some freeware, shareware, and commercial statistical tools available via the Internet and which could be used in QSAR for deriving models. Programming environments useful in Statistics, newsgroups and FAQs are also introduced due to their interest for the discipline.     

QSAR based modeling of inhibitory activity of alkenyldiarylmethane derivatives

A series of alkenyldiarylmethanes (ADAMs) were subjected to QSAR analysis by using linear free energy relationship model of Hansch. QSAR has been developed using steric, electronic and topological parameters along with appropriate dummy variable. Statistical techniques were applied to identify the structural and physicochemical requirements for ADAMs. The results are critically discussed on the basis of regression data and cross-validation techniques.     

Quantitative structure activity relationship (QSAR) studies on a series of imidazole derivatives as novel ORL1 receptor antagonists

QSAR studies were performed on a series of imidazole derivatives as novel Orl1 receptor antagonists. Imidazole derivatives have been analyzed in relation to their physicochemical and molecular properties. The activities of the compounds were found to be significantly correlated with the physicochemical parameters such as density (D), surface tension (St), index of refraction (Ior), balaban index (J) and partition coefficient (Log P). It was found that the presence of group at R₁ position was conducive for the inhibitory activity. The results are critically discussed on the basis of regression data and cross validation techniques. Poglani factor Q and the results of LOO (leave one out) method confirms the reliability and predictability of the proposed models.     

QSAR models for analgesic activity prediction of terpenes and their derivatives

Structural Chemistry - In the present study, quantitative structure-activity relationship (QSAR) models were developed to predict analgesic activity of some mono-/bicyclic terpenoids and their...     

SIA system employing the transient response from a potentiometric sensor array—Correction of a saline matrix effect

A Sequential Injection Analysis (SIA) system and an 8-potentiometric all-solid-state sensor array were coupled in a simple and automated electronic tongue device. The potentiometric sensors used were planar microfabricated structures with standard PVC membranes deposited onto a gold contact. The SIA system permitted the automated operation and generation of the calibration data, needed to build an Artificial Neural Network model, thanks to the precise dosing and mixing of volumes of stock solutions. The resolution of a four-ion mixture, i.e. ammonium, sodium, nitrate and chloride was the study case used for characterization of the system. Two different variants for signal acquisition, steady-state and transient recording, were arranged and compared. The dynamic treatment is shown to offer improved performance thanks to the benefits of the kinetic resolution. For this, it first extracts meaningful data from a FFT transform of each sensor's transient, which is then fed to an ANN model for estimation of each concentration in the four-ion mixture. While in a standard laboratory situation there was no difference between the two approaches, the dynamic treatment allowed the correction of a matrix effect in the case study, where an uncontrolled saline effect could be counterbalanced.     

中药复杂组效关系的变结构神经网络辨识方法

针对中药复杂组效关系的辨识问题,研究了变结构多层前馈神经网络,推导出一种新型的变结构网络学习算法,成功地应用于中药川芎药效活性预测计算.该方法从一个规模较小的网络出发,当网络无法达到预定的学习精度时,自动增加隐含层神经元个数,并在原有学习结果的基础上确定新的网络参数,自适应地确定前馈神经网络结构,可用于处理复杂化学模式信息.计算机仿真实验结果表明,该方法能有效地确定多层前馈神经网络的最佳结构,提高网络学习效率和函数逼近精度,解决复杂非线性函数映射关系准确建模问题.     

取代嘧啶衍生物抗前列腺癌活性的3D-QSAR研究

运用比较分子力场分析(CoMFA)方法,建立18种取代嘧啶衍生物抗前列腺癌活性(pM)的三维定量构效关系。训练集中15个化合物用于建立预测模型,测试集13个化合物(含10号模板分子和新设计的9个分子)作为模型验证。建立的CoMFA模型的交叉验证系数(R_ev²)、非交叉验证系数(R²)分别为0.344、0.935,说明所建模型具有较强的鲁棒性和良好的预测能力。该模型中立体场、静电场贡献率依次为71.6%、28.6%。影响取代嘧啶衍生物抗前列腺癌活性的主要因素是取代基的疏水作用和空间位阻,其次是取代基的库仑力、氢键及配位作用。基于此研究结果,设计了9个新化合物,其抗前列腺癌活性有待医学实验验证。     

QSAR,docking studies of 1,3-thiazinan-3-yl isonicotinamide derivatives for antitubercular activity

The enzyme – enoyl acyl carrier protein reductase (enoyl ACP reductase) is a validated target for antitubercular activity. Inhibition of this enzyme interferes with mycolic acid synthesis which is crucial for Mycobacterium tuberculosis cell growth. In the present work 2D and 3D quantitative structure activity relationship (QSAR) studies were carried out on a series of thiazinan–Isoniazid pharmacophore to design newer analogues. For 2D QSAR, the best statistical model was generated using SA-MLR method (r² = 0.958, q² = 0.922) while 3D QSAR model was derived using the SA KNN method (q² = 0.8498). These studies could guide the topological, electrostatic, steric, hydrophobic substitutions around the nucleus based on which the NCEs were designed. Furthermore, molecular docking was performed to gauze the binding affinity of the designed analogues for enoyl ACP reductase enzyme. Amongst all the designed analogues the binding energies of SKS 01 and SKS 05 were found to be −5.267 kcal/mol and −5.237 kcal/mol respectively which was comparable with the binding energy of the standard Isoniazid (−6.254 kcal/mol).     

人工神经网络应用于NO-3和NO-2两组分同时测定

应用人工神经网络原理,结合分光光度法,对吸收光谱严重重叠的硝酸根和亚硝酸根两组分进行同时测定,其结果令人满意。