Combinatorial Chemistry & High Throughput Screening - Volume 18, Issue 8, 2015

Paclitaxel is known as one of the most effective anticancer drugs. Near Infrared Spectroscopy (NIRS), a rapid, precise and non-destructive approach of analysis, has been widely used for qualitative and quantitative detection. The present study aims to analyze the plasma paclitaxel concentration with NIRS. Various batches of plasma samples were prepared and the concentration of paclitaxel was determined via high Read More

This work reports a detailed study of the ability of linear and non-linear classification methods to estimate the estrogenic activities of a series of 55 natural estrogen-like isoflavonoid and diphenolic compounds. In doing so, we examined the use of linear discriminant analysis (LDA) and nonlinear support vector machines (SVMs) techniques along with feature selection algorithms. The structural characteristics of each of the studied Read More

Methotrexate (MTX) is widely used to treat rheumatoid arthritis (RA), mostly along with non-steroidal anti-inflammatory drugs (NSAIDs), the most common of which is aspirin or acetyl salicylic acid (ASA). Since NSAIDs impair MTX clearance and increase its toxicity, it was necessary to develop a simple and reliable method for the monitoring of MTX levels in urine samples, when coadministered with ASA. The method was based o Read More

The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of Read More

Three-dimensional quantitative structure-activity relationship was developed for the series of compounds as malonyl-CoA decarboxylase antagonists (MCD) using the CoMFA and CoMSIA methods. The statistical parameters for CoMFA (q2=0.558, r2=0.841) and CoMSIA (q2= 0.615, r2 = 0.870) models were derived based on 38 compounds as training set in the basis of the selected alignment. The external predictive abil Read More

Quantitative relationships between structures of a set of p38 map kinase inhibitors and their activities were investigated by principal component regression (PCR) and principal componentartificial neural network (PC-ANN). Latent variables (called components) generated by principal component analysis procedure were applied as the input of developed Quantitative structure- activity relationships (QSAR) models. An e Read More

We describe here RepurposeVS for the reliable prediction of drug-target signatures using X-ray protein crystal structures. RepurposeVS is a virtual screening method that incorporates docking, drug-centric and protein-centric 2D/3D fingerprints with a rigorous mathematical normalization procedure to account for the variability in units and provide high-resolution contextual information for drug-target binding. Validity was c Read More

Data manipulation and maximum efficient extraction of useful information need a range of searching, modeling, mathematical, and statistical approaches. Hence, an adequate multivariate characterization is the first necessary step in investigation and the results are interpreted after multivariate analysis. Multivariate data analysis is capable of not only large dataset management but also interpret them surely and rapi Read More

Paecilomyces tenuipes, one of the commonly used Chinese medicinal fungus, has received much attention over the world, which possesses various active compounds and biological activities. However, little toxicological information is available. Therefore, the present study evaluated the potential toxicity of aqueous and ethanol extracts of Paecilomyces tenuipes N45 via acute and subchronic administration in mouse and r Read More

Among the chemometric tools used in rational drug design, we find artificial neural network methods (ANNs), a statistical learning algorithm similar to the human brain, to be quite powerful. Some ANN applications use biological and molecular data of the training series that are inserted to ensure the machine learning, and to generate robust and predictive models. In drug discovery, researchers use this methodology, looki Read More