How do I select QSAR descriptors?
Descriptors must be chosen in order to be as few as possible (generally for QSAR you should have maximum 1 descriptor every 5 molecules, better if more, of the training set,) and need to be uncorrelated between them.
What do you understand by QSAR and how can it be helpful in drug discovery?
Background. Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints.
What are the applications of QSAR models in biotechnology?
Also, in drug discovery and environmental toxicology, QSAR models are now regarded as a scientifically credible tool for predicting and classifying the biological activities of untested compounds, drug resistance, toxicity prediction and physicochemical properties prediction.
What is the role of QSAR in the screening of libraries?
The screening of chemical libraries with traditional methods, such as high-throughput screening (HTS), is expensive and time consuming. Quantitative structure–activity relation (QSAR) modeling is an alternative method that can assist in the selection of lead molecules by using the information from reference active and inactive compounds.
What are some examples of topological descriptors?
Examples of topological descriptors are: atom counts, ring counts, molecular weight, weighted paths, molecular connectivity indices, substructure counts, molecular distance edge descriptors, kappa indices, electro-topological state indices, and some other invariants [17].
Is QSAR a good prediction tool for studying drug activity?
In all mentioned articles QSAR study were good prediction tool for investigation drug activity or binding mode on specific receptors. Keywords Drug design, QSAR, QSPR, Molecular Descriptor, Coefficient of Determination R2, Squared Correlation Coefficient Q2.