Artificial intelligence platforms like ChatGPT and Dall.E have been capturing headlines this year with their ability to generate natural language content and even works of “art.”

But, AI and machine intelligence have also been transforming the way we discover and develop new drugs.

By using computational biology and artificial intelligence, companies are helping biopharmaceutical companies pick winning drug candidates and accelerate the drug development process.

Despite everything we’ve learned about human biology, chemistry, and how to build new drugs, the discovery and development process is still fraught with extreme risk and high rates of failure. 

Maybe only one in one thousand drug candidates succeed in making their way from discovery to commercialization. 

Even drugs that are deemed “good enough” for expensive clinical trials have abysmal success rates. 

Less than 10% of Phase 1 drugs make it to approval, and even drugs that make it to Phase 3 have failure rates in the range of 70%. 

Accounting for the high cost of failures, the price tag of getting a new drug to market can approach $2 billion in a process that can take a decade or more.

That's where computational biology and artificial intelligence can help pick winners and streamline the regulatory process.

This process works with biosimulation platforms. 

Artificially Stimulating the Human Body

A biosimulation platform is a software system that uses mathematical models and computational techniques to simulate biological processes and systems. 

These platforms can be used to model a wide range of biological systems, including cells, tissues, organs, and entire organisms.

With that, these platforms can help model disease behavior, simulate clinical trials in virtual human populations, and predict how drugs will perform in humans, all without the need for expensive, time-consuming, human and animal studies. 

The information gleaned from these virtual processes can help drug developers make better choices regarding which drugs to move forward with and how to do so.

Moreover, the regulatory paperwork required to shepherd a new therapy from the lab to the market is exhaustive, time-consuming, and expensive. 

As such, certain platforms include artificial intelligence-enabled writing technology that analyzes existing documents to help automate the process of generating new documents for regulatory purposes. 

The drug development market has increasingly turned to computational and AI-assisted services to help discover and develop new drugs, moreover, regulatory agencies are increasingly accepting data from such systems. 

And this is just the tip of the iceberg… AI can be used in a huge variety of ways within the biotechnology field. 

AI systems are now being used in medical imaging as well, such as CT or MRI scans, to identify potential abnormalities or diseases.

AI-powered systems can also be trained to assist in the diagnostic process by analyzing patient data and providing treatment recommendations.

AI-powered systems can comb through large amounts of medical data, including images, lab results, and patient histories, to accurately diagnose and predict diseases. 

As you could imagine, this would help doctors make more informed decisions and improve patient outcomes. 

Moreover, AI can be used to monitor the progression of diseases, allowing for early intervention and treatment.

AI applications can even go as deep as for use in gene editing, protein engineering, and computational biology. 

AI systems have shown promise in identifying potential targets where gene and protein editing could be effective, essentially leading to the development of more potent therapies for diseases such as cancer.

And the best part is, new applications are being developed every day. 

While some are fearful of a world where AI replaces jobs, I’m more optimistic about a world where humans and AI work alongside each other to increase productivity and efficiency across all disciplines.