treatments for diseases that are rare, complex, or novel. In the past, DR was

considered tedious, as target identification required manual selection through litera-

ture searches. Modern approaches have advanced to incorporating knowledge-based

and machine learning information into databases that can provide valuable output of

target molecules and networks and ultimately lead to the identification of a func-

tional FDA-approved drug target to reposition for the disease of interest. Some

challenges of DR include concerns regarding intellectual property and the integra-

tion of the multitudinal approaches for DR, in particular, to incorporate vast amounts

of data across various computational tools and databases with advanced expertise.

DR is being recognized to provide hopeful outcomes for neglected and orphan

diseases as well as significant potential for the future of personalized medicine.

Overall, genomic approaches confer a more efficient and effective establishment of

drug targets for DR.

Author Contributions Authors contributed equally to the writing and preparation

of the review manuscript. S.K.L. contributed to the supervision of this manuscript.

All authors have read and agreed to the published version of the manuscript.

Funding Internal funds from Monash Malaysia and School of Science Strategic

Grant 2020.

Conflicts of Interest The authors declare no conflict of interest.

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