# metadata.dc.description.abstract[].value
Stereochemistry plays a fundamental role in pharmacology. Here, we systematically investigate the relationship between stereoisomerism and bioactivity on over 1 M compounds, finding that a very significant fraction (~ 40%) of spatial isomer pairs show, to some extent, distinct bioactivities. We then use the 3D representation of these molecules to train a collection of deep neural networks (Signaturizers3D) to generate bioactivity descriptors associated to small molecules, that capture their effects at increasing levels of biological complexity (i.e. from protein targets to clinical outcomes). Further, we assess the ability of the descriptors to distinguish between stereoisomers and to recapitulate their different target binding profiles. Overall, we show how these new stereochemically-aware descriptors provide an even more faithful description of complex small molecule bioactivity properties, capturing key differences in the activity of stereoisomers.

# metadata.dc.relation.ispartof[].value
Journal Of Cheminformatics, 2024, vol. 16, num. 1

# metadata.dc.rights[].value
cc by (c) Comajuncosa Creus, Arnau et al, 2023

# metadata.dc.source[].value
Articles publicats en revistes (Institut de Recerca Biomèdica (IRB Barcelona))