This one-day hybrid workshop will introduce participants to the most relevant AI applications across the drug development lifecycle.
Details
- Type Event
- Date
- Time -
- Location University of California Washington Center @ 1608 Rhode Island Ave NW, Washington DC 20036 1st floor, MPR Hybrid: in-person & virtual attendance
About
Workshop Introduction
Drug development is changing rapidly with the advancement of artificial intelligence (AI). Across the R&D lifecycle, AI is beginning to reshape how organizations identify biological targets, design and optimize molecules, automate laboratory workflows, select patients, model dose and exposure, design and monitor clinical trials, prepare regulatory submissions, and generate post-market evidence. At the same time, AI introduces new expectations for validation, governance, workforce readiness, change management, and responsible implementation.
This one-day hybrid (in-person and Virtual attendance) ACDRS workshop will introduce participants to the most relevant AI applications across the drug development lifecycle, with emphasis on practical use cases, scientific and regulatory considerations, and the organizational capabilities required to move from experimentation to scaled impact.
Learning Objectives
By the end of the workshop, attendees should be able to:
- Describe high-value AI applications across discovery, preclinical, translational, clinical, regulatory, and commercial phases.
- Distinguish near-term operational AI use cases from longer-horizon frontier AI opportunities.
Identify key scientific, regulatory, quality, and governance considerations for AI-enabled drug development - Understand how AI may reshape clinical trial design, monitoring, regulatory interactions, evidence generation, and post-market lifecycle strategy.
Program
Workshop will take place in Washington DC at the University of California, Washington Center. Program begins at 9:00am eastern time and adjourns at 5:00pm eastern time.
| Time & Stage | Topic | Speakers |
|---|---|---|
| 9:00-9:10 AM Eastern Time | Welcome and Framing for the Day from the Workshop Chair | Charlie T. Gombar, PhD, ACDRS Director; and Brandon W. Higgs, PhD, Chief Data Officer, Diagnostics, Danaher Corporation |
9:10-9:50 AM Discov. to Transl. | AI Across Drug Development: From Models to Decision-Grade Evidence Focus: AI is increasingly being applied across drug development, from discovery and translational biomarkers to clinical trials, regulatory evidence, post-market learning, and enterprise adoption. But AI is not useful simply because it is impressive; it is useful when it improves a real decision, is supported by reliable evidence, is validated for its context of use, and is responsibly embedded into workflow. | Corina J. Shtir, PhD, CTO, Dexwell Inc. |
9:50-10:30 AM Preclinical / Molecular Design | Closing the Design-Build-Test-Learn Loop: AI and Automation Across Pharma and Life Science R&D Focus: From molecular design to laboratory automation, AI is reshaping how science is conducted. Drawing on examples from both pharmaceutical and reagent development, this talk will examine how organizations are combining machine learning, automation, and experimental data to accelerate innovation and build the next generation of AI-enabled R&D systems. | Christopher J. Langmead, PhD, Vice President of AI-Driven Molecular Design, Danaher |
| Networking Break | ||
10:45-11:25 AM Preclinical. | Beyond Designing Molecules: Can AI Learn Enough Biology to Transform Drug Development? Focus: This lecture will examine the transition from AI that predicts and designs molecules to AI that learns biology. We will explore why human biological response is intrinsically more difficult to model, requiring integration of genetic diversity, cellular context, space, time, feedback, perturbation biology, and high-dimensional functional measurements. Particular emphasis will be placed on the role of proteomics and human-relevant experimental systems in providing the biological ground truth required to train and validate these models. Finally, we will discuss a “lab-in-the-loop” paradigm in which computational prediction, experimental measurement, and iterative model refinement become a continuous learning cycle. The future of AI-driven drug development may depend as much on creating better biological measurements as on building better algorithms. | Andreas Huhmer, PhD, Senior Director Scientific Affairs and Alliance Management, Nautilus Biotechnology |
11:25 AM – 12:05 PM Translational/Imaging | Multimodal AI Imaging Biomarkers: Interpretable Precision Oncology from Clinic to Trial Focus: This talk examines the promise of these approaches, highlighted with use cases across multiple cancers, care settings, and data modalities. We will explore the requirements to make these tools trial- and clinic-ready: interpretability by design, grounding in tumor biology, and rigorous multi-site validation. Using QVT Score - a measure of tumor vascular complexity from routine CT - as a central example, we will show the multi-purpose utility of a biologically anchored radiomic tool. Discussed applications will include providing new prognostic data in the immunotherapy setting, monitoring outcomes by tracking treatment-induced vascular remodeling, and distinguishing anti-angiogenic from immune-driven mechanisms of action. We will also explore how AI tools can accelerate and augment standard clinical trial endpoints, such as RECIST. The session concludes with multimodal integration — fusing radiology with digital pathology, genomics, and clinical data for patient stratification. | Nate Braman, PhD, Vice President, AI Research & Development / Co-founder, Picture Health |
12:05-12:45 PM Clin. Pharm. | Agentic AI for Biopharma Workflows Focus: Agentic AI is starting to do real work across biopharma - regulatory writing, competitive intelligence, and quantitative analysis. This talk gives an honest view of what these systems can and can't do, then goes deep on one: a pharmacometrics agent that takes a raw dataset through NCA, population PK modeling, and simulation to an IND-ready report section | Husain Attarwala, PhD, VP Clinical Pharmacology & DMPK, Aera Therapeutics (virtual) |
| 12:45-1:45 PM | Lunch | |
1:45-2:25 PM Regulatory | AI Use for Regulatory Review – A Clinical Reviewer’s Perspective Focus: AI use cases in the regulatory review process, technical challenges and solutions of AI utilization | Glen Huang, MD, Assoc Director of Oncology Innovation, Oncology Center of Excellence, US Food and Drug Administration |
2:25-3:05 PM Commercial / Lifecycle | The Model Was Never the Moat - AI, Data, and Value Creation in Drug Development: What the Deals Returned, and How to Structure the Next Focus: As AI models commoditize, pharma bet that proprietary data was the new moat. So why have most of the marquee AI and data deals quietly disappointed? A business look at what these deals actually returned, why the winners were built differently, and what the real moat turns out to be. | Matthew Klusas, Managing Director, West Oxford Advisors |
| 3:05-3:20 PM | Networking Break | |
3:20-4:00 PM Govt Affairs / Public Policy | Balancing Openness and Control: Cross-border Health Data and AI Governance in China Focus: This talk examines how China’s overlapping data-security, privacy, health-data, and AI-governance regimes shape cross-border health-data transfers and impact international biomedical collaboration. It highlights how these rules affect firms operating in the pharmaceutical sector, and will provide insights on likely short-, medium-, and longer-term trajectories in China’s regulatory space. | Kenton Thibaut, PhD, Senior Resident China Fellow, Democracy + Tech Initiative, Atlantic Council (virtual) |
4:00-4:40 PM Enterprise Transformation | How AI Changes Work Across R&D, Job Redesign, Workforce Concerns, Upskilling, Operating Model Changes, Productivity Measurement, Change Management, and How to Navigate the Change Focus: This talk will explore how leaders can navigate this transition thoughtfully and pragmatically. We will examine job redesign, workforce concerns, upskilling, change management, and the organizational conditions required to convert AI investment into meaningful scientific outcomes. We will also consider how to measure productivity without reducing scientific value to speed alone. | John Conway, Founder & Chief Visioneer Officer, 20/15 Visioneers (virtual) |
| 4:40-5:00 PM | Closing Synthesis and Next Steps Key takeaways, ACDRS follow-up, and suggested next actions for attendees | Brandon Higgs |
Speaker Profiles
Registration
- Each year, in addition to its course, ACDRS also presents a workshop that is open to all interested parties.
- The registration fees for this course are shown below.
- The option to attend in-person or virtually via Zoom must be selected at time of registration.
- All registration fees are non-refundable. Registrations may be transferred to another individual if you are unable to attend.
- Program details and schedule are subject to change.
- Enrollment is limited.
- Deadline to register: Tuesday, October 13, 2026 11:59 pm PT
| Registration Category | Registration Fee |
|---|---|
| General Admission | $400.00 |
| Discounted fee for ACDRS alumni (industry) | $250.00 |
| Discounted fee for Govt/Academic/Nonprofit* | $50.00 |
* To be eligible for the limited Government/Academic rate, you must be currently affiliated with a university or government institution and sign up using an email address ending in .edu/.gov/.mil. For nonprofit, please contact [email protected].
For additional details, contact [email protected] .