Giovanni Xavier Perazzo
ABSTRACT
In a Rational Drug Design (RDD) one important step is the receptor-ligand interaction evaluation through molecular docking simulations. How it is a way impossible to test all available compounds for a target receptor, there is a need to select the most promising. One possible approach for such selection is to consider characteristics like a set of molecular properties called molecular descriptors. Aiming at describing these characteristics, we introduce a Data Warehouse (DW) model that integrates molecular descriptors from different public databases of compounds, as well as relates them with Virtual Screening (VS) experiments data. With the proposed DW we are able to produce proper data sets for classification mining experiments. We performed a case study with a VS considering as receptor the HIV-1 Protease receptor and 76 compounds. The data sets produced from our DW are composed by 7 molecular descriptors as the predictive attributes, and as a target attribute the discretized Free Energy of Binding (FEB) value between the ligands and the target receptor. By performing C4.5 algorithm over the generated data sets, we got decision-trees models that indicates which molecular descriptors and their respective values are relevant to influence on good FEB results.
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Index Terms
- A data warehouse as an infrastructure to mine molecular descriptors for virtual screening
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