Discovering Anti-Cancer Drugs via Computational Methods.

Shuguang Yuan,Yanhua Li,Wenqiang Cui,Qiuliyang Yu,Shouguo Wang,Adnane Aouidate

doi:10.3389/fphar.2020.00733

Abstract

New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are demanded for a new drug discovery via traditional drug development pipeline. How to reduce the research cost and speed up the development process of new drug discovery has become a challenging, urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged as a powerful, and promising technology for faster, cheaper, and more effective drug design. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. In this work, we discussed the different subareas of the computer-aided drug discovery process with a focus on anticancer drugs.

Highlights

INTRODUCTIONCancer remains a global and serious public health challenge. It is estimated that there are more than 200 different types of cancer, generally named according to the tissue where the cancer was recognized for the first time
Up to now, cancer remains a global and serious public health challenge
Since the successful development of HIV protease inhibitor Viracept in the USA in 1997, which was the first drug design fully driven by its target structure (Kaldor et al, 1997), computational methods have served as an essential tool in drug discovery projects and have been a cornerstone for new drug development approaches

Summary

INTRODUCTION

Cancer remains a global and serious public health challenge. It is estimated that there are more than 200 different types of cancer, generally named according to the tissue where the cancer was recognized for the first time. Since the successful development of HIV protease inhibitor Viracept in the USA in 1997, which was the first drug design fully driven by its target structure (Kaldor et al, 1997), computational methods have served as an essential tool in drug discovery projects and have been a cornerstone for new drug development approaches. The fast growth in computational power, including massively parallel computing on graphical processing units (GPUs), the continuous advances in artificial intelligence (AI) tools (Chan et al, 2019; Yang et al, 2019), have translated fundamental research into practical applications (Zhavoronkov et al, 2019) in the drug discovery field This attracted considerable attention for their outstanding performance on providing new promising perspectives and solutions to overcome life-threatening diseases. Websites https://www.drugbank.ca/ http://bidd.nus.edu.sg/group/ttd/ttd.asp http://matador.embl.de/ http://insilico.charite.de/supertarget/ http://tdrtargets.org/ http://www.dddc.ac.cn/pdtd/ https://www.ebi.ac.uk/chembldb http://stitch.embl.de/ http://www.bindingdb.org/ http://crdd.osdd.net/raghava/cancerdr/ http://www.cls.zju.edu.cn/dcdb/

Computational tools

Molecular Docking

Similarity Searching

QSAR Modeling

USING MD SIMULATION TO FIND NEW DRUG BINDING SITES

ARTIFICIAL INTELLIGENCE IN ANTICANCER DRUG DISCOVERY

SUCCESSFUL STORIES OF COMPUTATIONAL DRUG DISCOVERY

Niraparib Tosylate Monohydrate Ribociclib

CONCLUSION AND PERSPECTIVE