Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?

Abstract:

:The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the "undruggability" of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research. Graphical abstract Highlights of methods for assessing the druggability of biological targets.

journal_name

J Mol Model

authors

Agoni C,Olotu FA,Ramharack P,Soliman ME

doi

10.1007/s00894-020-04385-6

subject

Has Abstract

pub_date

2020-05-08 00:00:00

pages

120

issue

6

eissn

1610-2940

issn

0948-5023

pii

10.1007/s00894-020-04385-6

journal_volume

26

pub_type

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