Artificial intelligence is making it faster to get drug candidates to the clinic, but to gain a competitive advantage, companies must have a strong foundation of data.
Is artificial intelligence the new key to competitive advantage in pharma? You’d think so, based on the breathless coverage of advances and hopes in AI-powered drug discovery. The industry has been captivated by clinical trial milestones of AI-discovered drugs and the soaring valuations of companies in this space.
But when every company is buying the same game-changing tools, those tools lose their ability to differentiate. We shouldn’t discount AI’s potential to help patients. At the same time, pharma companies have always honed their competitive edge through creativity and ingenuity. AI won’t change that. Life sciences companies must stay focused on drug development fundamentals such as investing in R&D and effectively managing the resulting data, and leveraging their intellectual property to maximum advantage.
Close the Digital Transformation Gap
AI is only as good as the data feeding it, meaning that if companies don’t have the right data or if the underlying data aren’t robust enough, AI models cannot yield useful results. For life sciences companies, this includes data from the entire R&D lifecycle: flow data, assay data, protein data, data across a wide variety of instrumentation, and any other data that capture scientists’ insights, experiments, successes and failures. Only with sufficient information can researchers—with the help of AI or not—marry the right candidate and the right target to the right disease.
What’s more, AI won’t create any advantage for companies that are still struggling with the basics of data management. As CEO of life sciences software company Dotmatics, I’m well aware that you can’t code around bad business practices. AI needs clean, standardized, organized data to produce usable outputs. In turn, companies need a strong data governance culture to ensure that the data produced and collected by different teams are consistent. To that end, my company has developed a scientific intelligence data platform that flexibly aggregates all relevant data into intelligent data structures, enabling clean, reliable data analysis and paving the way for meta-analysis and AI-based algorithms.
Pharma has been called a digital laggard compared to other industries. McKinsey reports that life sciences companies “trail cross-industry leaders in digital maturity by a factor of two to three times.” This means that the pharmaceutical industry is significantly behind in adopting and integrating digital technologies compared to other sectors. This is often due to legacy systems and infrastructure and regulatory and compliance challenges.
That said, the industry made tremendous strides during the pandemic. Healthcare companies have increased their digital capabilities more than any industry except consumer goods since 2019.
Under pressure to deliver new treatments and to be multimodal in their research, life sciences companies have broken down silos that impede data sharing and digital collaboration. They’ve established digitalization goals that cascade from the C-suite to the research lab. But they’re still struggling to capture, integrate, and analyze data throughout the R&D lifecycle. PwC predicts that soon, “the ability to extract and manage value from data will significantly determine a biopharma company’s shareholder value.”
Even in a competitive landscape, life sciences businesses should push each other and the vendors that support them toward use of technologies that ultimately support multimodal research driving toward an AI future. It’s a rising tide that raises all boats. AI can help create a competitive advantage and reduce the average 10 years and $2.6 billion cost required to bring therapies to market.
Leverage Intellectual Property
Data also represent intellectual property (IP)—a pharmaceutical company’s lifeblood. A firm’s IP encapsulates its unique knowledge.
Conversely, AI is or will be a commodity. Vendors are building exciting AI products on top of public knowledge. Their models make it easier to glean relevant insights from chemical structures and properties of molecules, and push the starting point for research forward for every company that buys them. But when computational models built on public knowledge are used to design new drugs, debates will ensue around who owns or should own the resulting IP.
AI only becomes a unique advantage when it’s deployed atop a foundation of proprietary data that is optimized for use in R&D. When a company owns the data used to build a model, the IP and ownership are less contestable. That means life science businesses need to invest in R&D and the data science that powers it so their scientists and researchers can freely experiment, learn and iterate.
The biopharma industry is undergoing a sea change that holds promise for patients and providers. AI is set to make drug discovery faster and more cost-effective by augmenting scientists’ creativity, ingenuity and hard work. Now it’s up to life sciences companies to build a foundation on which AI-powered drug discovery can deliver on its potential.
