Abstract
Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We 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, we 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. We conclude that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years.
Highlights
The first decade of the 2000s has seen a consistent modification of the drug research landscape, due, among other aspects, to a rethinking of the drug discovery paradigm[1] and to the entrance into the era of Big Data.[2]
The pervasive digitalization of healthcare is providing quantitatively very important sources of phenotypic data, like primarily those contained in the electronic health records (EHRs) and those generated by wearable devices or apps.[33]
If we consider that a protein, like any other molecule, is an ensemble of interacting elements, it is immediately derived that it represents a complex system in which structure, dynamics, and eventually function can be viewed as emergent properties stemming from the relationships among the residues
Summary
The first decade of the 2000s has seen a consistent modification of the drug research landscape, due, among other aspects, to a rethinking of the drug discovery paradigm[1] and to the entrance into the era of Big Data.[2]. Moving to the field of human phenotypic data, we leave the territory of drug discovery to enter into the precision medicine arena In this context, the pervasive digitalization of healthcare is providing quantitatively very important sources of phenotypic data, like primarily those contained in the electronic health records (EHRs) and those generated by wearable devices or apps.[33] Limiting to EHRs, the information embedded in these documents includes the description of the health/disease status of individuals, clinical test results, drug prescriptions, and eventual adverse effects.[34] privacy issues limit the availability of this kind of data, and we cannot find publicly accessible databases yet. Computations are partitioned across clusters of machines that work in parallel and carry out the jobs in reasonable time and with high efficiency
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