摘要: We present the application of Feature Mining techniques to Developmental Therapeutics Program's AIDS antiviral screen database. The database consists 43576 compounds, which were measured for their capability protect human cells from HIV-1 infection. According these measurements, compounds classified as either active, moderately active or inactive. distribution classes is extremely skewed: Only 1.3 % molecules known be and 2.7 active.Given this database, we interested in molecular substructures (i.e., features) that are frequent molecules, infrequent inactives. In data mining terms, focused on features with a minimum support maximum inactive compounds. analyzed using levelwise version space algorithm forms basis inductive query system MOLFEA (Molecular Miner). Within framework, it possible declaratively specify interest, such frequency (possibly different) datasets well generality syntax them. Assuming detected causally related biochemical mechanisms, should facilitate development new pharmaceuticals improved activities.
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