<![CDATA[QSAR (quantitative structure-activity relationship) modeling is one of the well developed areas in drug development through computational chemistry. Similar molecules with just a slight variation in their structure can have quit different biological activity. This kind of relationship between molecular structure and change in biological activity is center of focus for QSAR Modeling. Predictions of property and/or activity of interest have the potential to save time, money and minimize the use of expensive experimental designs, such as, for example, animal testing. Intelligent machine learning techniques are important tools for QSAR analysis, as a result, they are integrated into the drug production process. The effective learnable model can reduce the cost of drug design significantly. The quantile estimation via neural network structure technique introduced in this paper is used to predict activity of pyrimidines based on the structure–activity relationship of these compounds which assist for finding potential treatment agents for serious disease. In comparison with statistical quantile regression, the qrnn significantly reduce the prediction error.]]>
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