45: Drug-DMsim: A Novel Pipeline for Inferring Drug MOA via Differential Module Similarity

Music City Center 

Uncovering the mechanism of action (MOA) of molecules is a pivotal aspect of drug discovery. Current methods, which rely on gene signatures or structural similarities to predict MOA, face substantial challenges, including the intricacies of gene expression and "Activity cliffs." To overcome these hurdles, we propose a novel approach named Drug Differential Modular Similarity (Drug-DMsim), which is designed to model the effects of drugs on the gene regulatory network (GRN) and infer MOAs from known drugs. This approach involves: (1) employing mutual information and partial correlation to independently reconstruct GRNs, (2) generating differential modularity scores to quantify the division strength of a GRN into distinct modules, and (3) utilizing a dimensionality reduction technique to map molecules onto a 2D space, facilitating the identification of patterns and clusters, and enhancing the interpretability and analysis of relationships between different molecules. By applying the proposed approach to LINCS datasets, we identified potential new drug targets. This novel approach advances our understanding of the molecular mechanisms of drugs and enables faster drug discovery.

Drug discovery

mechanism of action (MOA)

gene regulatory network (GRN)

Differential modularity

Dimensionality reduction

LINCS 

Section on Statistics in Genomics and Genetics