QSAR in Pharmaceutical Chemistry

Quantitative structure–activity relationship models (QSAR models) square measure regression or classification models employed in the chemical and biological sciences and engineering. Like alternative regression models, QSAR regression models relate a collection of "predictor" variables (X) to the efficiency of the response variable (Y), whereas classification QSAR models relate the predictor variables to a categorical price of the response variable. In QSAR modelling, the predictors contains physico-chemical properties or theoretical molecular descriptors of chemicals; the QSAR response-variable may be a biological activity of the chemicals. QSAR models initial summarize a supposed relationship between chemical structures and biological activity in an exceedingly data-set of chemicals. Second, QSAR models predict the activities of recent chemicals.

Related terms embody quantitative structure–property relationships (QSPR) once a property is modelled because the response variable. As associate degree example, biological activity may be expressed quantitatively because the concentration of a substance needed to relinquish an explicit biological response. in addition, once chemical science properties or structures square measure expressed by numbers, one will notice a mathematical relationship, or quantitative structure-activity relationship, between the 2. The mathematical expression, if fastidiously valid will then be wont to predict the modelled response of alternative chemical structures.

A QSAR has the shape of a mathematical model:

Activity = f(physiochemical properties and/or structural properties) + error

The error includes model error (bias) and data-based variability, that is, the variability in observations even on an accurate model.

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 has experienced a great evolution due to the development of specialized software and to the increasing computer power. The codification of the structural information of molecules through molecular descriptors and the subsequent data analysis allow establishing QSAR models (Quantitative Structure-Activity Relationship) that can be applied to the design and the virtual screening of new drugs. The development of sophisticated Docking methodologies also allows a more accurate predict of the biological activity of molecules. Moreover, through this type of computational techniques and theoretical approaches, it is possible to develop explanatory hypothesis on the mechanism of action of drugs.

  • QSAR methods: Statistical analysis and physicochemical properties
  • Data mining and knowledge discovery
  • Toxicogenomics / Molecular mechanisms
  • Transduction mechanisms and hormonal modulation at cellular level
  • Predicting environmental toxicity
  • Regulatory Use,Parameters and Validation of QSAR Models

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