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Scoring of de novo Designed Chemical Entities by Macromolecular Target Prediction
Author(s) -
Button Alexander L.,
Hiss Jan A.,
Schneider Petra,
Schneider Gisbert
Publication year - 2017
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201600110
Subject(s) - cheminformatics , pharmacophore , computer science , computational biology , chemistry , machine learning , artificial intelligence , combinatorial chemistry , computational chemistry , biology , stereochemistry
Computational de novo molecular design and macromolecular target prediction have become routine in applied cheminformatics. In this study, we have generated populations of drug template‐derived designs using ligand‐based building block assembly, and predicted their potential targets. The results of our analysis show that the reaction‐based de novo design generated new chemical entities with similar properties and pharmacophores as that of the template drugs as well as up to 44 % of the de novo compounds receiving the correct target predictions. Keeping in mind the probabilistic nature of the methods, such a combination of fast and meaningful computational structure generation by reaction‐based design and product scoring by target class prediction may be appropriate for prospective application in medicinal chemistry.