Premium
Finding Chemical Structures Corresponding to a Set of Coordinates in Chemical Descriptor Space
Author(s) -
Miyao Tomoyuki,
Funatsu Kimito
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.201700030
Subject(s) - chemical space , space (punctuation) , chemical structure , set (abstract data type) , chemical shift , biological system , training set , chemical species , computer science , chemical process , cheminformatics , artificial intelligence , chemistry , computational chemistry , biology , drug discovery , biochemistry , organic chemistry , programming language , operating system
When chemical structures are searched based on descriptor values, or descriptors are interpreted based on values, it is important that corresponding chemical structures actually exist. In order to consider the existence of chemical structures located in a specific region in the chemical space, we propose to search them inside training data domains (TDDs), which are dense areas of a training dataset in the chemical space. We investigated TDDs’ features using diverse and local datasets, assuming that GDB11 is the chemical universe. These two analyses showed that considering TDDs gives higher chance of finding chemical structures than a random search‐based method, and that novel chemical structures actually exist inside TDDs. In addition to those findings, we tested the hypothesis that chemical structures were distributed on the limited areas of chemical space. This hypothesis was confirmed by the fact that distances among chemical structures in several descriptor spaces were much shorter than those among randomly generated coordinates in the training data range