AI and the Future of Drug Development

Exclusive interview with health-law specialist Feruz Madaminov

Artificial Intelligence has become the defining force behind the next wave of pharmaceutical innovation. From identifying potential drug molecules to predicting clinical outcomes, AI tools have cut the early drug-discovery timeline from years to months — even weeks in some cases. Yet, the legal and ethical frameworks governing these technologies remain uncertain. What happens when an algorithm makes a life-impacting decision? Who bears responsibility — the software developer, the pharma sponsor, or the data scientist?

To discuss this evolving landscape, Discover Health spoke with Feruz Madaminov, a New York–based health-law specialist and LL.M. graduate of Penn Stated Dickinson Law, whose research focuses on the intersection of AI, drug development, and legal regulation.

– Feruz, the role of AI in pharmaceuticals has been growing rapidly. What would you say are the most recent breakthroughs that caught regulators’ attention?

– The most remarkable change is that AI is no longer just supporting scientists — it’s actually driving discovery.

In the past year, companies like Insilico Medicine and Nabla Bio demonstrated that AI can generate viable drug candidates in record time. Insilico reported developing an experimental molecule in just 21 days, while Nabla Bio, in partnership with Takeda Pharmaceuticals, announced an AI platform capable of designing and validating new antibodies within a few weeks — a process that traditionally takes several years.

Regulators have taken notice. In January 2025, the U.S. Food and Drug Administration released its Draft Guidance on Use of AI in Drug Development. For the first time, the agency formally acknowledged that AI tools are influencing nearly every stage of the drug-development lifecycle — from pre-clinical modelling to clinical-trial design and post-marketing safety monitoring.

That’s a profound regulatory moment. It means AI is now part of the regulated product ecosystem, and legal systems must start defining where algorithmic autonomy ends and human accountability begins.

– These are impressive developments, but they raise concerns. How is the U.S. dealing with issues like transparency and responsibility when AI tools are used in drug discovery?

– Exactly — that’s the central challenge.
The FDA’s draft guidance emphasizes “explainability” and data traceability — regulators want to see how an AI reached a decision. But that’s hard when models are black boxes. For example, if an AI tool recommends a specific compound structure and later it causes toxicity, we must ask: Was the algorithm flawed, or was the training data incomplete?

This question isn’t just theoretical. The European Medicines Agency (EMA) already warned that AI tools must undergo independent validation before being integrated into drug submissions. The U.S. hasn’t gone that far yet, but it’s moving there.

In practice, companies now keep “AI audit trails”, documenting every decision step of the algorithm. It’s a new kind of compliance documentation — one that merges tech with law.

– What legal risks do pharmaceutical companies face when using AI in drug development?

– Three stand out.
First, liability — if an AI error harms patients, traditional tort law doesn’t fit well. Is it product liability? Is it professional negligence? These boundaries are blurry.
Second, intellectual property — when an AI system designs a molecule, who owns it: the company, the programmer, or the machine itself? The U.S. Patent Office has already rejected AI-generated patents, while the EU is reconsidering its stance.
Third, data protection — AI models rely on massive health datasets. HIPAA compliance is the baseline, but AI often requires de-identified or synthetic data. Even then, privacy concerns remain when algorithms can re-identify patterns.

So, every legal department in the pharma industry now needs someone who can “speak both languages” — law and data science.

– Let’s talk about global implications. How might this affect emerging markets like Central Asia, where the pharmaceutical and biotech sectors are just beginning to scale?

– That’s a critical and often overlooked question.
Central Asia has enormous scientific and human potential, but the region’s regulatory ecosystems are still developing. Governments are modernizing their pharmaceutical laws, but most frameworks were designed for traditional manufacturing, not AI-driven innovation.

AI will force these countries to think beyond simple compliance and move toward risk-based governance — understanding that not all errors are human, and not all innovation should be constrained by fear of liability.

For example, when companies in the region begin collaborating with international biotech partners or conducting cross-border clinical trials, questions of data governance, ethical review, and intellectual property become much more complex. Without clear guidance, even promising projects can stall.

To stay competitive, Central Asia needs to invest in regulatory science, bioethics capacity, and legal harmonization across borders. Establishing regional standards or joint expert panels could help balance accountability with innovation.
In the end, AI is not just a technology challenge — it’s a test of institutional maturity and regional cooperation.

– Are regulators in the U.S. moving fast enough to keep up with the pace of AI-driven innovation?

– Honestly, regulators are doing their best to catch up, but innovation is moving faster.
The White House “AI Action Plan” (July 2025) sets a national framework for responsible AI use, and the FDA has started using AI internally for drug-review workflows. But regulation still relies heavily on voluntary industry disclosure.

That’s why collaboration is crucial. The FDA, the Department of Health and Human Services, and the National AI Initiative Office now conduct joint working groups with private companies and law schools — to shape what “AI compliance” will mean in the next decade.

I think the future of health law will look like a fusion of regulatory policy, computer ethics, and biotechnology — it’s the new multidisciplinary frontier.

– Finally, from a legal and ethical standpoint — what does the ideal future look like for AI in healthcare?

– The ideal scenario is accountable innovation.
We shouldn’t slow down AI because of fear, but we must build transparent, predictable rules that protect patients and encourage responsible use. AI should empower doctors, not replace them; and regulation should encourage disclosure, not punish it.

In the long run, countries that find that balance — between innovation and accountability — will lead the next era of healthcare. The U.S. is shaping that framework right now, and others, including Uzbekistan, can learn from its successes and mistakes.

As AI continues to reshape pharmaceutical research and development, the intersection of innovation and regulation becomes critical. For companies, regulators and jurisdictions alike, the next few years will determine who sets the standards — and who must adapt. Legal experts like Feruz Madaminov are playing a key role in navigating this evolving landscape.