Expediting the Design, Discovery, and Development of Anticancer Drugs using Computational Approaches
2018
Cancer is considered as one of the world’s leading cause of
morbidity and mortality. Over the past four decades, spectacular advances in
molecular and cellular biology have led to major breakthroughs in the field
of cancer research. However, the design and development of anticancer drugs
proves to be an intricate, expensive, and time-consuming process. To
overcome these limitations and manage large amounts of emerging data,
computer-aided drug discovery/design (CADD) methods have been developed.
Computational methods can be employed to help and design experiments, and
more importantly, elucidate structure-activity relationships to drive drug
discovery and lead optimization methods. Structure- and ligand-based drug
designs are the most popular methods utilized in CADD. Additionally, the
assimilation provided by these two complementary approaches are even more
intriguing. Nowadays, the integration of experimental and computational
approaches holds great promise in the rapid discovery of novel anticancer
therapeutics. In this review, we aim to provide a comprehensive view on the
state-of-the-art technologies for computer-assisted anticancer drug
development with thriving models from literature. The limitations associated
with each traditional in silico method have also been discussed, which helps
the reader to rationale the best computational tool for their analysis. In
addition, we will also shed some light on the latest advances in the
computational approaches for anticancer drug development and conclude with a
brief precis.
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