243: Why AI Still Hasn't Revolutionized Drug Discovery (Yet) | Thibault Geoui, PhD

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If AI is already being used across the drug development pipeline, why hasn’t its impact matched the investment?

AI can help researchers review scientific literature, predict protein structures, prioritize molecules, assess toxicity, support clinical trials, and monitor adverse events. But access to better tools doesn’t automatically create better drugs.

In this episode, I speak with Thibault Geoui, Science CDO and host of the Tech & Drugs Podcast, about where AI is making a practical difference in drug discovery and development—and where the results remain limited. 

We map AI across the full drug development funnel, from basic research and target identification to preclinical testing, clinical trials, regulatory documentation, commercialization, and pharmacovigilance.

We also discuss why digital-native tech-bio companies may be better positioned to benefit from AI than traditional pharmaceutical organizations. The difference isn’t simply the model. It’s how data, people, laboratory experiments, and AI tools are connected inside the workflow.

For digital pathology professionals, the conversation becomes especially relevant when we examine AI-powered biomarker development, the role of pathology in pharmaceutical research, and the Roche–PathAI case discussed in the episode.

And, of course, we talk about the problem every AI user eventually faces: an answer can look polished, specific, and completely convincing—and still be wrong.

Episode Highlights

00:00 — When convincing AI output creates more work
Why AI can accelerate information generation while increasing the time required for review and verification.

02:15 — From structural biology to science and technology leadership
Thibault shares his background in X-ray crystallography, structural biology, scientific data, and digital product development.

15:36 — Understanding the drug discovery and development funnel
How thousands of potential compounds are narrowed down through discovery, preclinical research, clinical trials, and approval.

20:00 — AI for scientific literature review
How alerts, filtering, summarization, and information extraction can help researchers manage a rapidly growing scientific literature base.

22:32 — AlphaFold and protein structure prediction
What faster access to predicted protein structures changes for researchers—and why structural prediction alone doesn’t solve drug discovery.

24:13 — Searching an enormous chemical space
How AI can help design and prioritize potential molecules for synthesis and experimental testing.

25:50 — Predicting efficacy and toxicity
Where AI supports preclinical research, why the models remain imperfect, and why experimental validation still matters.

29:38 — Has AI changed drug development outcomes yet?
A practical discussion about drug approval rates, AI investment, uneven returns, and the difference between deploying a tool and integrating it into a process.

34:33 — Why traditional pharma struggles to scale AI
Siloed data, legacy systems, organizational complexity, and the need to build reusable data workflows.

37:57 — The “lab in the loop” model
How tech-bio companies connect AI predictions with wet-lab experiments and feed the new data back into their models.

44:37 — Can tech-bio companies shorten development timelines?
How digital-native organizations are changing parts of the discovery and preclinical process.

58:00 — AI, pharma, and digital pathology
What the Roche–PathAI case discussed in the episode may indicate about the role of pathology data, biomarker discovery, and pharmaceutical workflows.

01:06:17 — AI errors in regulated environments
Why responsibility remains with the person or company submitting AI-assisted work, regardless of which tool produced it.

01:17:37 — The growing cost of AI tools
Subscriptions, token limits, model selection, AI orchestrators, and the need to use expensive tools more intentionally.

01:27:50 — What successful AI adoption requires
Starting with focused pilots, training scientists and technologists together, and treating implementation as organizational change.

01:30:26 — The AI quirks that still frustrate users
Hallucinated information, ignored writing instructions, stylistic habits, and poor awareness of time and context.

The episode’s timestamped themes and examples are documented in the supplied summary. The broader discussion covers AI from literature mining and molecular design through clinical development and post-market monitoring. 

Resources Mentioned

•  Thibault Geoui’s LinkedIn profile 
• Tech & Drugs Podcast
• MIT NANDA study on generative AI implementation and return on investment 
• Insilico Medicine as an example of a digital-native tech-bio company 

AI is already changing how scientific work gets done. The bigger question is whether organizations can redesign their workflows, train their teams, and maintain the human oversight needed to use it well.

Listen to the full episode for a practical look at AI in drug discovery, drug development, and digital pathology.

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