3 things to understand how AI might help develop new, cost-effective drug treatments

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The life sciences industry is right to be optimistic about the potential of generative AI. Biotech startups are already testing AI-generated drugs in clinical trials on human patients. Researchers estimate that AI-enabled drug discovery could add up to $50 billion in economic value over the next decade.

As the CEO of Dotmatics, a software company that builds technology for pharmaceutical scientists and researchers, I hope to reduce the time and cost of bringing new drugs to market, ultimately reducing the cost of treatment for patients. I’m excited about what’s coming.

But when it comes to AI, this is not a Cambrian moment. As with previous revolutionary technological advances, the journey toward the future of AI-assisted drug discovery will necessarily be deliberate, gradual, and full of ups and downs.

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We are already seeing setbacks. A schizophrenia drug discovered with an AI recently failed in two phase 3 clinical trials. It may take many years before drug discovery costs and timelines are measurably reduced. Especially since some estimates say that more than 20% of the costs come from clinical trials that need to be done manually.

Pharmaceutical companies still face significant challenges in making AI practical. (St. Petersburg)

And I worry that once the shine of AI fades, so will the interest of people outside the lab. Investors, governments, and journalists will play key roles in funding, regulating, and publicizing how AI will transform drug discovery.

So, as the life sciences industry works hard to realize the potential of AI, I urge everyone to remain cautious and optimistic.

1. The industry is (finally) ready to succeed

Life sciences has long been considered a laggard in the industry, but it is finally catching up in the race for digital transformation.

Pharmaceutical companies are adopting more efficient approaches to building databases, especially for electronic data capture (EDC), resulting in scalable and cost-effective infrastructure and tools for managing large amounts of data. You can access it. Traditional approaches to building this type of database take approximately 12 to 16 weeks.

Perhaps just as importantly, the life sciences ecosystem is finally coming together on the importance of digitalization. In February 2020, digital leaders surveyed by McKinsey reported that the biggest hurdle to convincing companies to change was a “lack of leadership support.” However, today, after the shock caused by the global coronavirus pandemic, that hurdle has hardly been raised.

2. Barriers still exist, and they are getting bigger

Strategic alignment and executive leadership are only the first steps. Pharmaceutical companies still face major challenges in bringing AI to life: a tsunami of data and complex new treatments.

Advanced research techniques generate increasingly large amounts of information. Genomics research is expected to generate 40 exabytes of data from twp within the next decade. (One exabyte is 1 billion gigabytes, or about 8.3 million iPhones (128 GB in size) worth of storage.) And that data is only growing faster.

Although this detailed data poses challenges in the short term, it provides tremendous value for drug development in the long term. Pharmaceutical companies must learn how to leverage AI to effectively leverage it in the lab. In addition to purchasing the right technology, organizations also need to ensure they practice data governance.

Scientists working on research

Pharmaceutical companies are building the foundation for scientists to develop rapid and cost-effective treatments. (St. Petersburg)

This includes designing data collection protocols with future reuse in mind. The research and development process for new treatments, such as monoclonal antibodies, mRNA vaccines, and gene editing, is more expensive and riskier than for traditional medicines.

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Life sciences companies must enable researchers to use knowledge gained from abandoned targets and clinical failures to make the continued development of new treatments cost-effective. yeah.

3. A series of small victories bring about change.

From technology upgrades to analyzing failed clinical trial results, all of this work is manual, time-consuming, and difficult. Frankly, it’s going to be tough.

Life sciences has long been considered a laggard in the industry, but it is finally catching up in the race for digital transformation.

But that’s what makes progress meaningful. By investing in platforms and processes that enable the practical application of AI in the lab, pharmaceutical companies are laying the foundation for a future where scientists develop rapid and cost-effective treatments. . Each new drug candidate, whether successful in clinical trials or not, represents a step toward improving the health and quality of life for people with both common and rare diseases.


Keep in mind that the introduction of ChatGPT is also not a Cambrian moment. His idea for large-scale language models dates back to the 1960s. Computer scientists and chip designers have been working quietly and diligently for decades to make the release of ChatGPT possible. Along the way, advances in data storage and processing have been introduced that have changed the way we live and work.

Pharmaceutical companies’ journey towards successful application of AI will be punctuated by the same small victories that bring about transformative change. Hard-working scientists and researchers should recognize and celebrate this incremental progress, and so should the rest of us.

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