Quantitative structure–activity relationship - Wikipedia
So far, a couple of identified DPP IV inhibitors, such as sitagliptin and saxagliptin, . On the basis of the common structure and the reported SAR analyses of the the 3D-QSAR models were generated using the CoMFA program of Sybylx SARANEA: a freely available program to mine structure-activity and structure- selectivity relationship information in compound data sets. Lounkine E(1), Wawer M. present research work for QSAR study, software such as chemdraw Type-2 diabetes involves the use of more stable GLP-1 mimetics, e.g. exenatide The Quantitative structure activity relationship (QSAR) of substance is.
Quantitative structure–activity relationship
Diabetes mellitus is a diverse and complicated disorder that is characterized by persistent hyperglycemia. Hypoglycemic medication is used to lower the blood sugar level in the body or to treat other severe symptoms of diabetes mellitus.
These medications can be categorized into insulin and insulin preparations, which are used only parenterally, and hypoglycemic medicine, which can be administered orally [ 3 ]. The National Diabetes Statistics Report revealed that from tothe number of American diabetic patients increased from The International Diabetes Federation recently reported that the number of people with diabetes is expected to rise from million to million by Most people with diabetes live in low- and middle-income countries [ 5 ].
Although there are anti-diabetic medications currently approved by the U. FDA to treat patients with type 2 diabetes, most do not achieve appropriate glycemic control, and some have severe side effects.
Successful treatment of type 2 diabetes, therefore, requires new drugs with improved mechanisms of action.
Current Status of Computer-Aided Drug Design for Type 2 Diabetes
In this review, we describe the use of computational tools for the discovery and design of new anti-diabetic drugs that are not currently approved, but that may lower glucose levels and decrease the risk of hypoglycemia, which is a major obstacle to glucose control and a special concern for therapies that increase insulin levels.
From toonly 50 compounds were approved by the FDA in the U. This suggests that experimental libraries made by conventional high-throughput screening take more time, and that the results are not always efficient for developing novel drugs. Computer-aided drug design provides advantages for experimental findings, mechanisms of action and new suggestions for molecular structures for new synthesis, and it can help in making cost-effective decisions before the costly process of drug synthesis begins.
Computer-aided drug design can increase the hit rate of novel anti-diabetic drug-like compounds because it better uses a large chemical search space to find a suitable target compared with traditional high-throughput screening and combinatorial chemistry.
Several studies have compared conventional high-throughput screening and virtual screening, and virtual screens had hit rates of tenfold to fold those of conventional screening [ 10 - 14 ]. Computational methods are required because the amount of biological data has increased and manual screening against such data requires much time and human resources.
Computer-aided drug design methods have been used in the development of therapeutic molecules for over three decades. The increasing use of this method is reflected in the number of publications about computer-aided drug design in fatal diseases.
Publications on computer-aided drug design for the top 3 most fatal diseases [ 15 ] are shown in Fig. Data mining approach[ edit ] Computer SAR models typically calculate a relatively large number of features.
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Because those lack structural interpretation ability, the preprocessing steps face a feature selection problem i. Feature selection can be accomplished by visual inspection qualitative selection by a human ; by data mining; or by molecule mining.
A typical data mining based prediction uses e. Molecule mining approaches, a special case of structured data mining approaches, apply a similarity matrix based prediction or an automatic fragmentation scheme into molecular substructures.
Furthermore, there exist also approaches using maximum common subgraph searches or graph kernels. Matched molecular pair analysis Typically QSAR models derived from non linear machine learning is seen as a "black box", which fails to guide medicinal chemists. Recently there is a relatively new concept of matched molecular pair analysis  or prediction driven MMPA which is coupled with QSAR model in order to identify activity cliffs.
QSARs are being applied in many disciplines, for example: Any QSAR modeling should ultimately lead to statistically robust and predictive models capable of making accurate and reliable predictions of the modeled response of new compounds. For validation of QSAR models, usually various strategies are adopted: The success of any QSAR model depends on accuracy of the input data, selection of appropriate descriptors and statistical tools, and most importantly validation of the developed model.
Validation is the process by which the reliability and relevance of a procedure are established for a specific purpose; for QSAR models validation must be mainly for robustness, prediction performances and applicability domain AD of the models.
For example, leave one-out cross-validation generally leads to an overestimation of predictive capacity. Even with external validation, it is difficult to determine whether the selection of training and test sets was manipulated to maximize the predictive capacity of the model being published.
Different aspects of validation of QSAR models that need attention include methods of selection of training set compounds,  setting training set size  and impact of variable selection  for training set models for determining the quality of prediction. Development of novel validation parameters for judging quality of QSAR models is also important.
A simple example is the relationship between the number of carbons in alkanes and their boiling points.
There is a clear trend in the increase of boiling point with an increase in the number carbons, and this serves as a means for predicting the boiling points of higher alkanes. Biological[ edit ] The biological activity of molecules is usually measured in assays to establish the level of inhibition of particular signal transduction or metabolic pathways.
Drug discovery often involves the use of QSAR to identify chemical structures that could have good inhibitory effects on specific targets and have low toxicity non-specific activity. Of special interest is the prediction of partition coefficient log P, which is an important measure used in identifying " druglikeness " according to Lipinski's Rule of Five. While many quantitative structure activity relationship analyses involve the interactions of a family of molecules with an enzyme or receptor binding site, QSAR can also be used to study the interactions between the structural domains of proteins.