Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs

TL;DR

This study presents a comprehensive data-driven approach linking chemical structure descriptors of 1159 drugs to their gene expression effects in cancer cells, revealing systematic relationships and specific structure-response patterns.

Contribution

It introduces an integrative model that correlates 3D chemical descriptors with biological responses, advancing understanding of structure-function relationships in drug effects.

Findings

Identified ten major chemical-biological response components.

Linked hydrophobic features to DNA damage response.

Discovered cell line-specific responses to certain chemical features.

Abstract

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.