Some Perspectives on Network Modeling in Therapeutic Target Prediction

TL;DR

This paper discusses the intersection of network modeling and graph algorithms in therapeutic target prediction, emphasizing recent advances and fostering collaboration between biological and computational research communities.

Contribution

It provides a perspective on modeling and algorithmic approaches for therapeutic target identification, highlighting underexplored algorithmic advances to strengthen interdisciplinary ties.

Findings

Highlights key graph algorithmic techniques used in target prediction

Identifies gaps and opportunities for algorithmic innovation in the field

Encourages collaboration between biological and graph algorithm communities

Abstract

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities.