Speeding the process

Generated image using the prompt

AI tools help researchers discover new drugs more quickly

Source: Indianapolis Business Journal

Travis Johnson punched a few computer keys and, a moment later, his monitor glowed with what appeared to be an abstract painting, with swirls of green, red, orange and blue.

It wasn’t modern art but a data visualization showing changes in a mouse’s brain when it is fed a high-fat diet—a basic research step in trying to discover a new drug to treat obesity.

The colorful images on Johnson’s screen, powered by a supercomputer from Indiana University, were generated from about one terabyte of data—equivalent to tens of millions of rows of data if it were loaded into a spreadsheet.

A stack of printer paper with all that data would soar as high as 44 Empire State Buildings, said Johnson, director of bioinformatics at the Indiana Biosciences Research Institute and assistant professor of biostatistics at the Indiana University School of Medicine.

“If you had 100 people, with average reading speed, reading for eight hours a day every day, it would take them 17.6 years to finish reading all this data,” he added.

But by using artificial intelligence tools, Johnson and his colleagues can, in just two or three days, process all the data into visualizations and produce a list of genes associated with disease states and cell types, which can then be used as a candidate list for novel drug targets.

At the research institute, a not-for-profit based at 16 Tech innovation district on the western edge of downtown, scientists and data experts are using AI tools to search through reams of information to find new ways to treat conditions from obesity to Alzheimer’s disease.

So are thousands of other researchers at medical centers, pharmaceutical companies, biotech companies and AI specialty firms, all in the race to find better drugs and treatment for diseases.

The reality of using AI for drug discovery looks less like AI imagined it (top) and more like Travis Johnson, director of bioinformatics at the Indiana Biosciences Research Institute, working with AI-analyzed data on a computer. (IBJ photo/Chad Williams)

Making the most of AI

Artificial intelligence is not new to drug making. Pharmaceutical and biotech firms have been working with early forms of it for years.

But a raft of new AI tools, including generative and predictive technologies, are turbocharging the process. During the last nine years, the cumulative amount of AI investments across the pharma and biotech sectors has increased by almost thirtyfold, to $24.6 billion as of last December, according to Deep Pharma Intelligence, a London-based market research firm.

Drug discovery—the process of identifying drug candidates for testing—is notoriously time-consuming and expensive. It often costs hundreds of millions of dollars and typically takes three to six years before a drug can even be tested in humans.

But a group of AI tools is revolutionizing nearly every stage, with the potential to cut years and millions of dollars out of the process.

At the early stage of drug discovery, the AI tools are used to identify certain proteins or genes that can counteract certain diseases. They do that by doing the type of grunt work no human could do: analyzing troves of genomic data, health records, medical imaging, clinical trials and publication.

In the middle stage, the tools hold the potential to examine huge libraries of molecules and predict key properties, such as toxicity, bioactivity and chemical characteristics of molecules.

Finally, in the late stage, they might generate entirely new molecules from scratch.

In the case of the biosciences institute’s research into obesity, digging into the huge collection of mouse-cell data is an AI approach to finding answers in a mountain of information that would otherwise be all but inaccessible.

“Artificial intelligence is a way to kind of reorganize and regroup this data so that you can have an understanding of what’s happening in the cell,” said Mary Mader, the institute’s vice president of molecular innovation and a former oncology researcher at Eli Lilly and Co. “You can do it in a way that the older-style methods of analysis would make it much more laborious.”

Venture capital companies are pouring billions of dollars into small and growing AI vendors and AI-driven biotechs, which are, in turn, forming an increasing number of research partnerships with large drugmakers.

“Since 2017, there has been an obvious shift in the perception from skepticism and cautious interest all the way to a realization of a strategic role AI has to play in the emerging ‘‘data-centric’ model of innovation,” according to Deep Pharma Intelligence’s overview report last year.

The change in perception was underpinned by a number of factors, including several commercial successes and milestones, reached mostly by smaller “end-to-end” AI drug discovery companies.

To read the full article, go to the Indianapolis Business Journal (subscription required).