Genomics: How Can Employing AI Enhance Healthcare?

The most expensive part of drug research is discovery, and improving drug candidate quality increases clinical trial success rates. A genomics-driven approach could enhance disease biology understanding, minimize R&D costs and time to market, and bring more targeted drugs to patients faster.

FREMONT, CA : Genomics is allowing for more personalized treatment by revealing which genes are involved in different medical conditions.  Scientists are using genomics to figure out how Covid-19 spreads and influences the immune system, for example. This information can aid in the development of vaccines.

The data produced by a single human genome sequence ranges from 300GB to 1TB. Over the last decade, technological advancements have driven down the cost of sequencing, a previously expensive procedure, and resulted in an explosion of genomic data. This has opened up a plethora of possibilities for Artificial Intelligence (AI) in space. Companies are attempting to commercialize AI-based genomics solutions to produce improved pharmaceuticals, improve disease detection, and help clinicians identify the most appropriate therapies.

Drug discovery and screening

According to one study, AI in drug discovery will be a 40 billion dollar market by 2027. 

The most expensive part of drug research is discovery, and improving drug candidate quality increases clinical trial success rates. A genomics-driven approach could enhance disease biology understanding, minimize R&D costs and time to market, and bring more targeted drugs to patients faster.

One firm, for example, already has several AI-driven drug candidates in clinical trials. Pharma companies normally invest a significant amount of money in research and development on drugs, many of which will fail in clinical trials, in the hopes of finding one that will excel and achieve blockbuster returns (that is, 1 billion dollars in annual sales).

AI-based drug discovery and screening has the ability to transform the ‘blockbuster’ business model. When combined with genomics, AI can help companies develop medicines for rare diseases, which are far smaller markets that are often overlooked due to high production costs and poor return on investment.

Since biological processes are fundamentally complex, they produce multidimensional data that humans can find difficult to interpret. On the other hand, AI is well-equipped to identify trends in this type of data, and startups are using it to enhance every phase in the drug development process, from biological target identification to preclinical testing.