Abstract
Virtual screening of molecules is one of the hot topics in life science. Often, molecules are encoded by descriptors with numerical values as a basis for finding regions with a high enrichment of active molecules compared to non-active ones. In this contribution we demonstrate that a simpler binary version of a descriptor can be used for this task as well with similar classification performance, saving computational and memory resources. To generate binary valued rules for virtual screening, we used the GenIntersect algorithm that heuristically determines common properties of the binary descriptor vectors. The results are compared to the ones achieved with numerical rules of a neuro-fuzzy system.
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Paetz, J. (2006). Generalization Rules for Binarized Descriptors. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds) Biological and Medical Data Analysis. ISBMDA 2006. Lecture Notes in Computer Science(), vol 4345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11946465_7
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DOI: https://doi.org/10.1007/11946465_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68063-5
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