Regulation Flow Analysis discovers molecular mechanisms of action from large knowledge databases

Abstract

Drug development is a long and expensive process, with only a small fraction of potential drugs being finally approved. The challenge of drug development is rooted in our limited understanding of biological systems and the disease processes that drugs are trying to modulate. We propose a novel method, called Regulation Flow Analysis (RFA), which is based on the principles of biological regulation, causal graphs, and graph flow. RFA is able to generate causal hypotheses of mechanism of action, using large Knowledge Graphs (KG) of molecular regulation. Our numerical experiments demonstrate that the generated hypotheses, in the form of regulation pathways, often summarize mechanisms of drug action in a manner both understandable and actionable. Thus, RFA can greatly improve our understanding of the biological processes underlying health and disease, and therefore substantially facilitate drug development. Our examples illustrate how RFA recovers known mechanisms of action and can be used for target and biomarker discovery and validation.