Current Topics in Medicinal Chemistry - Volume 8, Issue 18, 2008

Nowadays, it is well known that Quantitative studies on Structure-Activity and Structure-Property relationships (QSAR/QSPR) may become powerful tools to help Medicinal Chemistry scientist in directed drug research. In past years, various strategies have been developed to characterize and classify structural patterns of low weighted drugs by means of molecular descriptors. It has become possible not only to assess diversitie Read More

The search for new compounds with a given biological activity requires enormous effort in terms of manpower and cost. This effort arises from the large number of compounds that need to be synthesized and subsequently biologically evaluated. For this reason the pharmaceutical industry has shown great interest in theoretical methods that enable the rational design of pharmaceutical agents. In the last years bioinformatics ha Read More

As the development of the Human Genome Project (HGP), the sequencing of whole human genome has been completed, and a series of human genes have been detected, both of which result in the naissance of pharmacogenomics. Pharmacogenomics is the study of how an individual's genetic inheritance affects the body's response to drugs using the information of human genomics and bioinformatics approaches. It is Read More

Artificial neural networks (ANNs) have been widely used for medicinal chemistry modeling. In the last two decades, too many reports used MATLAB environment as an adequate platform for programming ANNs. Some of these reports comprise a variety of applications intended to quantitatively or qualitatively describe structure-activity relationships. A powerful tool is obtained when there are combined Bayesian-regularized neur Read More

Variable selection is a procedure used to select the most important features to obtain as much information as possible from a reduced amount of features. The selection stage is crucial. The subsequent design of a quantitative structure-activity relationship (QSAR) model (regression or discriminant) would lead to poor performance if little significant features are selected. In drug design modern era, by the means of com Read More

In order to minimize expensive drug failures, is essential to determine potential activity, toxicity and ADME problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of potential drug is advisable even before synthesis using computational techniques such as QSAR modeling. A great number of in silico approaches to activ Read More

Good progress has been made to modernize traditional Chinese medicines by obtaining active components from natural herbs. In this review, some recent works on procuring active components and modernizing traditional Chinese medicines will be covered. In addition, some recent works on drug design using modern drug design tools have been described. With some well defined targets, the traditional Chinese medicine data Read More

In recent times, there has been an increased use of software and computational models in Medicinal Chemistry, both for the prediction of effects such as drug-target interactions, as well as for the development of (Quantitative) Structure-Activity Relationships ((Q)SAR). Whilst the ultimate goal of Medicinal Chemistry research is for the discovery of new drug candidates, a secondary yet important outcome that results is in the cr Read More

The method MARCH-INSIDE (MARkovian CHemicals IN SIlico DEsign) is a simple but efficient computational approach to the study of Quantitative Structure-Activity Relationships (QSAR) in Medicinal Chemistry. The method uses the theory of Markov Chains to generate parameters that numerically describe the chemical structure of drugs and drug targets. This approach generates two principal types of parameters Stoc Read More

The drug discovery and development process is lengthy and expensive, and bringing a drug to market may take up to 18 years and may cost up to 2 billion $US. The extensive use of computer-assisted drug design techniques may considerably increase the chances of finding valuable drug candidates, thus decreasing the drug discovery time and costs. The most important computational approach is represented by structure Read More

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