Various genetic or epigenetic changes cause autoimmune disorders, infectious

diseases, transplantation, and even cancer by DNA methylation and altered miRNA

expression. Mutated epigenetic regulators, such as histone acetylation and methyla-

tion, are the most impaired epigenetic pathways in cancer. These abnormal epige-

netic modifications in cancer have been revealed by sequencing chromatin

modifications with deep sequencing technologies. Scientists are focusing on devel-

oping personalized drugs using “omics technologies.” Appropriate biomarkers are

needed to carry out specific therapies with each patient. Scientists are highly

optimistic about “omics-based” healthcare interventions because knowledge in the

fields of genomics and transcriptomics as well as understanding of the potentials of

modified proteomes has grown significantly. Some of the essential biomarkers, such

as altered gene expression signatures, germline or somatic gene variations (i.e.,

polymorphisms, mutations), chromosomal defects, and chosen protein biomarkers,

functional disorders with a genetic etiology, are used to select therapies for patients;

which are linked to pharmacogenomic knowledge available in medication labeling

(Chow 2017). New advancements in proteomics have the potential not only to

improve health outcomes but also to reduce the expense of therapies (Matthews

et al. 2016; Aravanis et al. 2017; Quezada et al. 2017). One such technique is liquid

biopsy, which involves collecting and analyzing molecules from body fluid, such as

urine, sweat, whole blood, serum, and plasma. A large number of biomarkers, such

as circulating tumor cells (CTC); circulating tumor proteins; cell-free DNA

(cf-DNA); cell-free RNA (cf-RNA), including microRNAs (miRNAs); and extra-

cellular vesicles, especially exosomes, have been identified as circulating molecules.

These biomarkers effectively recognize the very early stages of cancer, preneoplastic

disorders, etc., demonstrating their practical necessity for patient survival

(Moutinho-Ribeiro et al. 2019) (Fig. 1.1).

1.5

Speedy Drug Discovery with Artificial Intelligence

(AI) and Machine Learning

Artificial intelligence is widely used in the drug discovery process due to its many

capabilities. AI will gather and interpret biomedicine knowledge quite effectively to

adopt patient-driven biology and accumulate data for deriving more predictive

hypotheses. It helps in the development of novel patient-specific drugs by specifi-

cally identifying and validating drug targets. It may also be used to repurpose

medications, thus increasing research and development quality (R&D). Artificial

intelligence is being used to track down drug targets and therapies for disorders such

as Parkinson’s disease and diabetes. It can solve both simple and complicated

problems by learning from its past solutions and personified experience. As a result,

advances in AI technology, along with dramatically increased computational

resources, are revolutionizing the drug discovery process (Fleming 2018; Mak and

Pichika 2019).

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Emerging Technologies: Gateway to Understand Molecular Insight of. . .

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