Title:Expediting the Design, Discovery and Development of Anticancer Drugs using Computational Approaches
VOLUME: 24 ISSUE: 42
Author(s):Shaherin Basith, Minghua Cui, Stephani J.Y. Macalino and Sun Choi*
Affiliation:National Leading Research Laboratory of Molecular Modeling & Drug Design (NLRL), College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, National Leading Research Laboratory of Molecular Modeling & Drug Design (NLRL), College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, National Leading Research Laboratory of Molecular Modeling & Drug Design (NLRL), College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, National Leading Research Laboratory of Molecular Modeling & Drug Design (NLRL), College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760
Keywords:Cancer, anticancer, drug, structure-based, ligand-based, integration, pharmacophore, drug repositioning,
polypharmacology.
Abstract:Cancer is considered as one of the world's leading causes 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 prove 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 can help 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.
