Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review

By Peter Csermely, Tamás Korcsmáros, Huba J. M. Kiss, Gabor London, Ruth Nussinov. Pharmacology and Therapeutics, 2012

844 citations recorded by the source corpus.

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/687370b408ceaaf5906fe1bb1ed58f99a38659e8

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Citations in the corpus

Visualization of genetic disease-phenotype similarities by multiple maps t-SNE with Laplacian regularization (2014, background): “Studying the associations between diseases not only help us to find their common genetic basis [8], but also provide novel insights into molecular mechanisms [9] and future drug targets for pharmaceutical research [10].”
Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. (2014, background): “16 22 – 25 Therefore, development of a machine learning-based model using multi-dimensional drug properties might be a promising strategy to predict unknown DDIs.”
Synchronization in complex networks and its application - A survey of recent advances and challenges (2014, background): “Although synchronization or controllability has been applied in other fields such as serving as a novel paradigm of drug discovery in molecular networks (Csermely et al., 2013), understanding cancer progression and to develop effective anti-cancer therapies (Cornelius et al.”
Integrating personalized gene expression profiles into predictive disease-associated gene pools (2017, background): “Instead, multi-target strategies may prove more promising for drug development.(34) Our approach can be used to quantitatively assess the expected fraction of patients for which a drug is expected to be effective, helping guide the development of targets with maximal efficacy.”
Network propagation: a universal amplifier of genetic associations (2017, background): “A network module, the member genes of which are associated with a particular disease. applications, including gene function prediction 6,21 , module discovery 22 , disease characterization 23,24 and drug target prediction 25 .”
The H-index of a network node and its relation to degree and coreness (2016, background): “…catastrophic outages in power grids or the Internet 14 , optimize the use of limited resources to facilitate information propagation 15 , discover drug target candidates and essential proteins 16 , and design strategies for communication breakdowns in human and telecommunication networks 17 .”
Vital nodes identification in complex networks (2016, background): “Indeed, to identify vital nodes associated with some certain structural or functional objectives is very significant, which allows us to better control the outbreak of epidemics [13, 14], conduct successful advertisements for e-commercial products [15, 16], prevent catastrophic outages in power grid”
Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, background): “These knowledge bases have proven beneficial to many other studies - for example, in drug discovery [141] and clinical information extraction [142].”
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning (2017, background): “Administering drug combinations instead of monotherapy can lead to an increased efficacy compared to single drug treatments (Csermely et al. , 2013; Jia et al. , 2009).”