In silico study of natural antioxidants.

Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants.

Inhibition activity prediction for a dataset of candidates' drug by combining fuzzy logic with MLR/ANN QSAR models.

A hybrid of artificial intelligence simple and low computational cost QSAR was used. Approximately 90 pyridinylimidazole-based drug candidates with a range of potencies against p38R MAP kinase were investigated. To obtain more flexibility and effective capability of handling and processing information about the real world, in this case, the fuzzy set theory was introduced into the QSAR.

Computer-aided drug design to explore cyclodextrin therapeutics and biomedical applications.

Cyclodextrin (CD) is a subset of the macrocyclic structural class, which is an important class of small organic agents that are useful functional excipients. They have wide range application possibilities in different fields of sciences such as material preparation, medicine, analytical chemistry, and separation processes. They are used widely in pharmaceutical formulations and drug delivery for increasing the water solubility of low soluble drugs and drug candidates.

Dual-acting of Hybrid Compounds - A New Dawn in the Discovery of Multi-target Drugs: Lead Generation Approaches.

Finding high quality beginning compounds is a critical job at the start of the lead generation stage for multi-target drug discovery (MTDD). Designing hybrid compounds as selective multitarget chemical entity is a challenge, opportunity, and new idea to better act against specific multiple targets. One hybrid molecule is formed by two (or more) pharmacophore group's participation.

Computer Aided Drug Design for Multi-Target Drug Design: SAR /QSAR, Molecular Docking and Pharmacophore Methods.

Multi-target drugs against particular multiple targets get better protection, resistance profiles and curative influence by cooperative rules of a key beneficial target with resistance behavior and compensatory elements. Computational techniques can assist us in the efforts to design novel drugs (ligands) with a preferred bioactivity outline and alternative bioactive molecules at an early stage. A number of in silico methods have been explored extensively in order to facilitate the investigation of individual target agents and to propose a selective drug.

Application of Multivariate Linear and Nonlinear Calibration and Classification Methods in Drug Design.

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 rapidly.

A quantitative structure property relationship for prediction of solubilization of hazardous compounds using GA-based MLR in CTAB micellar media.

QSPR studies for estimating the incorporation organic hazardous compounds in cationic surfactant (CTAB) were developed by application of the structural descriptors and multiple linear regression (MLR) method. Various structure-related descriptors were studied in order to derive information on hydrophobic, electronic and steric properties of solute molecules. Theoretical molecular descriptors selected by genetic algorithms-procedure were followed to predict logKs values by a stepwise-MLR method.

Prediction of gas chromatography/electron capture detector retention times of chlorinated pesticides, herbicides, and organohalides by multivariate chemometrics methods.

A quantitative structure-retention relationship (QSRR) study, has been carried out on the gas chromatograph/electron capture detector (GC/ECD) system retention times (t(R)s) of 38 diverse chlorinated pesticides, herbicides, and organohalides by using molecular structural descriptors. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and partial least squares (PLS) regression. The stepwise regression using SPSS was used for the selection of the variables that resulted in the best-fitted models.