Discovery science is essential for driving breakthroughs and revealing insights that traditional approaches might overlook. By helping scientists uncover patterns and correlations in data, the methodology aids in the formation of new hypotheses.
For example, a team of researchers might analyze large-scale genomic data to identify patterns associated with disease, without a predetermined hypothesis in mind. That analysis could then reveal unexpected gene variants linked to certain diseases, or new biological pathways that researchers can later test with targeted hypotheses.
Historically, discovery science has largely been driven by trial and error, with researchers relying on unstructured exploration as they search for meaning in massive data sets. But this approach is incredibly time-consuming and costly, which limits the amount of discovery-based research scientists can conduct—and therefore slows innovation. It can take several years (or decades, even) for scientists’ hunches to eventually develop into tangible solutions.
Chris McSpiritt
But now, AI is changing the rules of discovery. In fact, 98 percent of life sciences organizations are leveraging generative AI, and 95 percent are using agentic AI—the highest adoption rate among highly regulated industries. AI has the potential to drastically reduce the time from discovery to translational research outcomes. For instance, drug discovery timelines could potentially be condensed from an average of 5-6 years into just a single year. In addition to accelerating drug discovery timelines, AI also has the potential to reduce R&D costs by 25-50 percent.
Essentially, AI will greatly lessen the Herculean effort of the discovery process, bringing more life-saving drugs, therapeutic treatments, and insights to the forefront faster than ever before. Here’s how:
One of the most significant challenges of discovery-based research is determining what to focus on in the first place. The robust analysis power of large language models (LLMs) is invaluable for helping researchers make more educated initial guesses about which areas will be most impactful to explore. For example, AI can help scientists rapidly review literature, surface critical focus areas, and formulate hypotheses much faster than traditional methods allow.
AI has enhanced the speed and accuracy of predictive modeling. This allows researchers to conduct initial drug design and compound molecule design faster and more precisely. By doing more modeling up front, they can better predict how drugs will behave in the body. This enhanced accuracy helps them narrow down the most promising contenders earlier in clinical trials, saving time and money, and ultimately supporting better patient outcomes. There is also a global effort underway to use AI to model human cells, which has been described as “the holy grail of biology” in terms of its promise.
AI is making life sciences more iterative by helping researchers rapidly learn from both successes and failures. These insights can then be integrated into future experiments, eliminating the need to start from scratch each time. Additionally, AI’s ability to learn over time allows it to suggest experimental designs and optimize procedures. It also facilitates the sharing of new findings across teams, ensuring that knowledge is continuously updated and accessible to both early-stage and advanced researchers. This fosters a collaborative environment not only between human teams, but also between humans and their AI “coworkers.”
Despite the huge potential of AI in discovery science, challenges remain. Life sciences companies have historically been reluctant to share information, while academic and public institutions may be more open, making data access, governance, and collaboration a challenge. These disconnects can slow the discovery process, limit the scope of discovery, and delay the development of new therapies if left unaddressed.
Additionally, integrating (sometimes proprietary) models and tools that don’t interface effectively with one another can become a roadblock. There must also be a way to make provision and control access to those different models so that researchers can collaborate securely without compromising data integrity or intellectual property.
To unlock AI’s true potential, life sciences organizations need a unified platform that enables parallel experimentation and makes insights accessible across different teams and organizations—while upholding governance.
Moving from trial-and-error to targeted discovery requires a strategy that fosters human and AI collaboration, strengthens security, and is backed by the right technical foundation. With the right approach, life sciences organizations can unlock unprecedented innovation and bring life-saving solutions to the forefront faster than ever before.
Chris McSpiritt is vice president of life sciences strategy at Domino Data Lab.
The post How AI is Accelerating the Scientific Method from Trial and Error to Targeted Discovery appeared first on GEN - Genetic Engineering and Biotechnology News.
For example, a team of researchers might analyze large-scale genomic data to identify patterns associated with disease, without a predetermined hypothesis in mind. That analysis could then reveal unexpected gene variants linked to certain diseases, or new biological pathways that researchers can later test with targeted hypotheses.
Historically, discovery science has largely been driven by trial and error, with researchers relying on unstructured exploration as they search for meaning in massive data sets. But this approach is incredibly time-consuming and costly, which limits the amount of discovery-based research scientists can conduct—and therefore slows innovation. It can take several years (or decades, even) for scientists’ hunches to eventually develop into tangible solutions.
Chris McSpiritt
But now, AI is changing the rules of discovery. In fact, 98 percent of life sciences organizations are leveraging generative AI, and 95 percent are using agentic AI—the highest adoption rate among highly regulated industries. AI has the potential to drastically reduce the time from discovery to translational research outcomes. For instance, drug discovery timelines could potentially be condensed from an average of 5-6 years into just a single year. In addition to accelerating drug discovery timelines, AI also has the potential to reduce R&D costs by 25-50 percent.
Essentially, AI will greatly lessen the Herculean effort of the discovery process, bringing more life-saving drugs, therapeutic treatments, and insights to the forefront faster than ever before. Here’s how:
Zeroing in on areas ripe for experimentation
One of the most significant challenges of discovery-based research is determining what to focus on in the first place. The robust analysis power of large language models (LLMs) is invaluable for helping researchers make more educated initial guesses about which areas will be most impactful to explore. For example, AI can help scientists rapidly review literature, surface critical focus areas, and formulate hypotheses much faster than traditional methods allow.
AI has enhanced the speed and accuracy of predictive modeling. This allows researchers to conduct initial drug design and compound molecule design faster and more precisely. By doing more modeling up front, they can better predict how drugs will behave in the body. This enhanced accuracy helps them narrow down the most promising contenders earlier in clinical trials, saving time and money, and ultimately supporting better patient outcomes. There is also a global effort underway to use AI to model human cells, which has been described as “the holy grail of biology” in terms of its promise.
Challenges and considerations
AI is making life sciences more iterative by helping researchers rapidly learn from both successes and failures. These insights can then be integrated into future experiments, eliminating the need to start from scratch each time. Additionally, AI’s ability to learn over time allows it to suggest experimental designs and optimize procedures. It also facilitates the sharing of new findings across teams, ensuring that knowledge is continuously updated and accessible to both early-stage and advanced researchers. This fosters a collaborative environment not only between human teams, but also between humans and their AI “coworkers.”
Despite the huge potential of AI in discovery science, challenges remain. Life sciences companies have historically been reluctant to share information, while academic and public institutions may be more open, making data access, governance, and collaboration a challenge. These disconnects can slow the discovery process, limit the scope of discovery, and delay the development of new therapies if left unaddressed.
Additionally, integrating (sometimes proprietary) models and tools that don’t interface effectively with one another can become a roadblock. There must also be a way to make provision and control access to those different models so that researchers can collaborate securely without compromising data integrity or intellectual property.
To unlock AI’s true potential, life sciences organizations need a unified platform that enables parallel experimentation and makes insights accessible across different teams and organizations—while upholding governance.
Moving from trial-and-error to targeted discovery requires a strategy that fosters human and AI collaboration, strengthens security, and is backed by the right technical foundation. With the right approach, life sciences organizations can unlock unprecedented innovation and bring life-saving solutions to the forefront faster than ever before.
Chris McSpiritt is vice president of life sciences strategy at Domino Data Lab.
The post How AI is Accelerating the Scientific Method from Trial and Error to Targeted Discovery appeared first on GEN - Genetic Engineering and Biotechnology News.