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Mapping of Activity through Dichotomic Scores (MADS): A new chemoinformatic approach to detect activity‐rich structural regions
Author(s) -
Todeschini Roberto,
Consonni Viviana,
Ballabio Davide,
Grisoni Francesca
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2994
Subject(s) - weighting , benchmark (surveying) , computer science , projection (relational algebra) , cheminformatics , space (punctuation) , artificial intelligence , binary number , theoretical computer science , computational biology , data mining , machine learning , algorithm , mathematics , bioinformatics , biology , physics , geography , cartography , arithmetic , acoustics , operating system
A new chemoinformatic approach, called Mapping of Activity through Dichotomic Scores, is introduced. Its goal is the supervised projection of molecules, represented with strings of binary digits expressing the presence or absence of selected structural features, onto a novel 2‐dimensional space, which highlights regions of active (inactive) molecules of interest. At the same time, variables are projected onto a second 2‐dimensional space, which highlights those structural features that are more related to the molecular activity of interest. Unlike the classical weighting schemes used in substructural analysis, which consider the substructures independently of each other, the Mapping of Activity through Dichotomic Scores approach considers the interactions between pairs of substructures, that is, their frequencies of cooccurrence in the molecules. In this work, the theory is presented and elucidated, with an example dataset and in comparison with a benchmark fragment‐based scoring scheme.