New models involving quantum chemical parameters for assessing the chromatographic retention process

.Knowledge about the theoretical relationship between the analyte properties and the critical chromatographic parameters is mandatory for a better interpretation of the separation mechanism and a more leisurely development of quantitative studies. In a preliminary stage of this work, we introduce the Gumbel distribution, the extreme value distribution type-I widely used in other fields, as a novel tool for modelling the chromatographic peak shape. Further, we develop mathematical models to evaluate the effect of the experimental variables and various quantum parameters on the chromatographic indices, such as the retention time, capacity factor, asymmetry factor, tailing factor and number of theoretical plates. Finally, we propose a mechanistic behaviour for the chromatographic separation process based on the structure-retention relationship of fifteen selected drugs involving several molecular quantum parametersS

Quantitative structure activity relationships in computer aided molecular design

The drug development process requires the complete evaluation and identification of the chosen substance as well as its properties. It involves extensive chemical examination to achieve the best therapeutic effects which demands huge expenditure both in terms of time and money. Computer aided molecular design (CAMD) allows the production of new substances with pre-decided properties. Additionally, in order to illustrate and determine the interrelationship between the chemical structure of a compound and its biological activity, Quantitative Structure Activity Relationship (QSAR) is applied by employing a mathematical model and arranging molecular descriptors. This paper presents review of CAMD and QSAR techniques. The most common chemometric techniques are also emphasized. CAMD and QSAR are considered to be extremely efficient instruments in molecular design and accelerate the initial steps of drug development process. Furthermore, they enhance the effectiveness and reduce the cost of newly developed drugs

Prediction of n-octanol-water partition coefficient for polychlorinated biphenyls from theoretical molecular descriptors

A quantitative structure-property relationship (QSPR) study was performed to develop models that relate the structures of 133 polychlorinated biphenyls to their n-octanol-water partition coefficients (log Kow). Molecular descriptors were derived solely from 3D structures of the molecules. The genetic algorithm-partial least squares (GA-PLS) method was applied as a variable selection tool.  The partial least square (PLS) method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network model. These descriptors are: Balabane index (J), XY Shadow (SXY), Kier shape index (order 3) (3к), Wiener index (W) and Maximum valency of C atom (VmaxC). The use of descriptors calculated only from molecular structure eliminates the need for experimental determination of properties for use in the correlation and allows for the estimation of log Kow for molecules not yet synthesized. The root mean square errors for ANN predicted partition coefficients of training, test and external validation sets were 0.063, 0.112 and 0.126, respectively, while these values are 0.230, 0.164 and 0.297 for the PLS model, respectively. Comparison between these values and other statistical parameters for these two models revealed the superiority of the ANN over the PLS model

Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT1A selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies

A Similarity Based Approach for Chemical Category Classification

This report aims to describe the main outcomes of an IHCP Exploratory Research Project carried out during 2005 by the European Chemicals Bureau (Computational Toxicology Action). The original aim of this project was to develop a computational method to facilitate the classification of chemicals into similarity-based chemical categories, which would be both useful for building (Q)SAR models (research application) and for defining chemical category proposals (regulatory application).JRC.I-Institute for Health and Consumer Protection (Ispra

Recent Applications of Quantitative Structure-Activity Relationships in Drug Design

One of the most important challenges that face medicinal chemists today is the design of new drugs with improved properties and diminished side-effects for treating human disease such as AIDS and others. Medicinal chemists began the process by taking a lead structure and then finding analogs exhibiting the preferred biological activities. Next, they used their experience and chemical insight to eventually choose a nominee analog for further development. This process is difficult, expensive and took a long time. The conventional methods of drug discovery are now being supplemented by shortest approaches made possible by the accepting of the molecular processes involved in the original disease. In this view, the preliminary point in drug design is the molecular target which is receptor or enzyme in the body as an option of the existence of known lead structure

Review of Computational approaches for predicting the physicochemical and biological properties of nanoparticles

In the growing field of nanotechnology there is a need to determine the physicochemical and potential toxicological properties of nanomaterials since many industrial, medical and consumer applications are based on an understanding of these properties and on a controlled exposure to the materials. This document provides a literature review on the current status of computational studies aimed at predicting the physicochemical properties and biological effects (including toxicity) of nanomaterials, with an emphasis on medical applications. Although a number of models have been published for physicochemical property prediction, very few models have been published for predicting biological effects, toxicity or the underlying mechanisms of action. This is due to two main reasons: a) nanomaterials form a colloidal phase when in contact with biological systems making the definition and calculation of properties (descriptors) suitable for the prediction of toxicity a new and challenging task, and b) nanomaterials form a very heterogeneous class of materials, not only in terms of their chemical composition, but also in terms of size, shape, agglomeration state, and surface reactivity. There is thus an urgent need to extend the traditional structure-activity paradigm to develop methods for predicting the toxicity of nanomaterials, and to make the resulting models readily available. This document concludes by proposing some lines of research to fill the gap in knowledge and predictive methodologyJRC.I.6-Systems toxicolog