  Detailed and systematic understanding of the biological effects of millions
of available compounds on living cells is a significant challenge. As most
compounds impact multiple targets and pathways, traditional methods for
analyzing structure-function relationships are not comprehensive enough.
Therefore more advanced integrative models are needed for predicting biological
effects elicited by specific chemical features. As a step towards creating such
computational links we developed a data-driven chemical systems biology
approach to comprehensively study the relationship of 76 structural
3D-descriptors (VolSurf, chemical space) of 1159 drugs with the gene expression
responses (biological space) they elicited in three cancer cell lines. The
analysis covering 11350 genes was based on data from the Connectivity Map. We
decomposed these biological response profiles into components, each linked to a
characteristic chemical descriptor profile. The integrated quantitative
analysis of the chemical and biological spaces was more informative about
protein-target based drug similarity than either dataset separately. We
identified ten major components that link distinct VolSurf features across
multiple compounds to specific biological activity types. For example,
component 2 (hydrophobic properties) strongly links to DNA damage response,
while component 3 (hydrogen bonding) connects to metabolic stress. Individual
structural and biological features were often linked to one cell line only,
such as leukemia cells (HL-60) specifically responding to cardiac glycosides.
In summary, our approach identified specific chemical structures shared across
multiple drugs causing distinct biological responses. The decoding of such
systematic chemical-biological relationships is necessary to build better
models of drug effects, including unidentified types of molecular properties
with strong biological effects.
