DOI
https://doi.org/10.1021/acs.jmedchem.9b01989

Summary
Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, the authors illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. They first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, that can be used to identify putative drug−target interactions without resorting to 3D modeling. Finally, they present some aspects of Boolean networks dynamics, anticipating that it might become a very potent modeling framework to develop in silico screening protocols able to simulate phenotypic screening experiments. In the conclusion the affirm that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years.

Relevance for Complex Systems Knowledge
Several networks need to be considered to study System of pharmaceutical interest: i) Networks for the Analysis of Molecules Data Sets; ii) Protein Structure Networks; iii) Human Disease Network and Drug Discovery. These networks are simplified representations of complex systems dealing with the problem of missing information, a common situation in the study of biological systems. The missing data can be recovered using several prediction methods and the network dynamics that can be applied to a new approach to the drug discovery process (even if largely over-unexplored since now).

Point of Strength
This paper suggests that the classical computational approaches to drug design should be more efficiently employed if integrated in a network context.