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Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea
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Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: F. Javier Luque
Molecules 2018, 23(8), 1963; https://doi.org/10.3390/molecules23081963
Received: 17 July 2018 / Revised: 3 August 2018 / Accepted: 4 August 2018 / Published: 6 August 2018
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their “undruggable” binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery. View Full-Text
Keywords: protein-protein interaction; peptidomimetics; hot spots; network analysis; machine learning; docking; virtual screening; fragment-based design; molecular dynamics protein-protein interaction; peptidomimetics; hot spots; network analysis; machine learning; docking; virtual screening; fragment-based design; molecular dynamics
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MDPI and ACS Style

Macalino, S.J.Y.; Basith, S.; Clavio, N.A.B.; Chang, H.; Kang, S.; Choi, S. Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery. Molecules 2018, 23, 1963. https://doi.org/10.3390/molecules23081963

AMA Style

Macalino SJY, Basith S, Clavio NAB, Chang H, Kang S, Choi S. Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery. Molecules. 2018; 23(8):1963. https://doi.org/10.3390/molecules23081963

Chicago/Turabian Style

Macalino, Stephani J.Y., Shaherin Basith, Nina A.B. Clavio, Hyerim Chang, Soosung Kang, and Sun Choi. 2018. "Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery" Molecules 23, no. 8: 1963. https://doi.org/10.3390/molecules23081963

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