Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets

Journal of Chemical Information and Modeling
Jameed Hussain, Ceara Rea

Abstract

Modern drug discovery organizations generate large volumes of SAR data. A promising methodology that can be used to mine this chemical data to identify novel structure-activity relationships is the matched molecular pair (MMP) methodology. However, before the full potential of the MMP methodology can be utilized, a MMP identification method that is capable of identifying all MMPs in large chemical data sets on modest computational hardware is required. In this paper we report an algorithm that is capable of systematically generating all MMPs in chemical data sets. Additionally, the algorithm is computationally efficient enough to be applied on large data sets. As an example the algorithm was used to identify the MMPs in the approximately 300k NIH MLSMR set. The algorithm identified approximately 5.3 million matched molecular pairs in the set. These pairs cover approximately 2.6 million unique molecular transformations.

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Citations

Jan 9, 2013·Journal of Computer-aided Molecular Design·Ye HuJürgen Bajorath
Feb 7, 2013·Journal of Computer-aided Molecular Design·Bernardo Palacios-BejaranoMiguel Ángel Gómez-Nieto
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Mar 23, 2012·Journal of Chemical Information and Modeling·Disha Gupta-OstermannJürgen Bajorath
Apr 12, 2012·Journal of Chemical Information and Modeling·Xiaoying HuJürgen Bajorath
Jun 5, 2012·Journal of Chemical Information and Modeling·Anne Mai WassermannJürgen Bajorath
Aug 8, 2012·Journal of Chemical Information and Modeling·Dagmar Stumpfe, Jürgen Bajorath
Aug 17, 2012·Journal of Chemical Information and Modeling·Kathrin HeikampJürgen Bajorath
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