Artificial intelligence (AI) continues to create new possibilities across life sciences, and few areas make that clearer than drug discovery. At the recent MichBio 2026 Michigan Drug Discovery & Development Symposium, Zach Weingarden, MS, Director of AI Technology & Applications at TrialAssure, joined a panel of experts to discuss how AI is reshaping the path from target identification through preclinical development, clinical research, and patient impact.
The session, titled “AI and the Future of Drug Discovery: Revolutionizing the Path from Target to Therapeutic,” featured moderator Andrew Kocab, PhD, of Dvant Pharma, along with panelists Gerry Higgins, PhD, MD, of Phenomics AI, Duxin Sun, PhD of the University of Michigan, Alice Walker, PhD of Wayne State University, and Weingarden.
Much of the discussion centered on the remarkable ways AI is being used earlier in the development process, especially in drug discovery and molecular design. One example shared during the conversation made the power of AI especially clear. Walker, a computational chemist, described how designing and testing 10 molecules in a year was considered an aggressive pace using conventional methods. Today, AI can help teams evaluate millions of molecules in the same timeframe, dramatically expanding the scale and speed of early discovery.
That shift represents one of AI’s most powerful contributions to life sciences. It can help researchers explore a much larger field of possibilities, identify promising candidates sooner, and make better use of time and resources before therapies move further through development.
At the same time, the discussion also showed that AI’s role in life sciences extends far beyond discovery.
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AI in later stages of drug development
For TrialAssure, Weingarden brought a valuable perspective to the panel by speaking to how AI can support the later stages of the development pipeline, where clinical, regulatory, transparency, and documentation needs become increasingly complex. While molecule design and discovery use cases are often easier to visualize because of their scale, AI’s ability to support clinical operations, regulatory workflows, and transparency processes can be just as meaningful for organizations seeking to bring therapies to patients more efficiently.
“AI in drug discovery is exciting because the scale is immediately clear,” said Weingarden. “When you move further down the development pipeline, the value of AI becomes just as important, but the focus shifts. It becomes about helping teams manage complex information, improve consistency, preserve traceability, and support high-quality decision-making across regulated workflows.”
That message resonated throughout the session. Regardless of where AI is being implemented, the same principles continue to matter. Transparency, traceability, security, and change management are central considerations for any organization seeking to use AI responsibly in life sciences.
AI can help teams move faster, but speed alone is rarely the only goal. In regulated environments like pharma and biotech, organizations also need to understand how outputs are created, how information is used, who reviews the work, and how processes are documented. Those transparency and traceability requirements apply whether AI is being used to screen molecules, support medical writing, improve trial transparency, or assist with other clinical development workflows.
This is where TrialAssure’s perspective becomes especially relevant. The company’s work with AI-enabled solutions is built around the idea that advanced technology should support expert teams while keeping humans in control of key decisions. In areas such as medical writing, disclosure, anonymization, and data sharing, AI must be practical, secure, and aligned with the needs of these teams.
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Questions the industry is asking
For Weingarden, the panel also reinforced a growing reality across the industry. Life sciences organizations are asking similar questions about AI, even when their specific use cases differ, such as:
- How can we use AI responsibly?
- How do we protect sensitive information?
- How do we maintain confidence in the output?
- How do we bring teams along through change?
- How do we make sure AI fits into existing workflows instead of creating new complexity?
Those questions are becoming a common thread across the industry.
As AI continues to mature, its value will likely be measured not only by what it can generate, but by how well it supports the people and processes behind clinical development. The greatest opportunities will come when organizations pair technical capability with thoughtful implementation, strong oversight, and a clear understanding of where human expertise remains essential, according to Weingarden.
The MichBio panel gave insight to the fact that AI is already changing what is possible across the drug development journey, from how new therapies are discovered to how clinical and regulatory teams manage the information required to advance them. The organizations that will gain the most value from AI will be those that pair innovation with the trust, structure, and responsible practices needed to bring new capabilities into this field.
For TrialAssure, that responsibility is central to the future of AI in life sciences. The company remains focused on helping organizations use AI in ways that are secure, transparent, traceable, and practical for the teams responsible for moving therapies forward.
Click to request a demo of TrialAssure’s AI-enabled solutions.
