In-silico Predictive Mutagenicity Model Generation Using Supervised Learning Approaches

Abstract

With the advent of High Throughput Screening techniques, it is feasible to filter possible leads from a mammoth chemical space that can act against a particular target and inhibit its action. Virtual screening complements the in-vitro assays which are costly and time consuming. This process is used to sort biologically active molecules by utilizing the structural and chemical information of the compounds and the target proteins in order to screen potential hits. Various data mining and machine learning tools utilize Molecular Descriptors through the knowledge discovery process using classifier algorithms that classify the potentially active hits for the drug development process.
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