How AI is Reshaping Pharmaceutical Investment: Innovation Over Cost Reduction

[writing exercise based on insight from MFS Investment Management, 2026]

As artificial intelligence transforms drug discovery, investors face a critical question: Which companies will capitalize on this revolution? Drawing on MFS Investment Management's pharmaceutical research, this analysis explores how AI is reshaping drug development economics and creating new investment opportunities.

New Drugs Drive Stock Performance

Understanding pharmaceutical investments requires grasping one principle: new drugs drive drug stocks. "When I first started 12 years ago, I had this epiphany and I thought to myself, drugs drive drug stocks," explains Mike Jones, MFS equity research analyst. "Fast-forward about 10 years, what I realized is, it's actually not drugs that drive drug stocks, it's new drugs that drive drug stocks."

This distinction matters because every drug operates on borrowed time, patents expire within 10-15 years. The average cost of bringing a new drug to market reaches $2.6 billion, with timelines spanning 10-15 years (DrugBank, 2024). This creates a "treadmill" business model where companies must continuously innovate to replace declining revenue streams.

The drug development process unfolds across three FDA-regulated phases (U.S. Food and Drug Administration, n.d.): Phase 1 (20-80 volunteers) emphasizes safety and metabolism. Phase 2 (several hundred patients) focuses on effectiveness. Phase 3 (hundreds to thousands) definitively answers whether the drug works. The economics are sobering: approximately 2,000 Investigational New Drug (IND) applications are filed annually, yet only 40-50 drugs receive FDA approval, a 2-3% success rate (U.S. Food and Drug Administration, n.d.).

"It gets very expensive, very quickly," notes Jones. "Drug companies are always trying to figure out optimal ways to increase the probability of success, lower the cost, fail quickly, and ultimately deliver innovations for patients."

AI Improves Innovation Rather Than Reducing Prices

Artificial intelligence is revolutionizing pharmaceutical R&D by enhancing innovation, not lowering consumer prices. AI encompasses machine-based systems making predictions based on defined objectives (U.S. Food and Drug Administration, 2024), with machine learning (ML), techniques training algorithms from data, proving particularly valuable.

AI methodologies transforming drug discovery include deep neural networks (DNNs) analyzing molecular structures, support vector machines (SVMs) predicting toxicity, and AlphaFold, which predicts protein structures with unprecedented accuracy (National Institutes of Health, 2023). "Using AI, we can rapidly analyze our proprietary splicing database of over 14 million splicing events across thousands of RNA-seq samples to uncover valuable drug targets," explains Maria Luisa Pineda, Envisagenics CEO. "This enables us to transform transcriptomic complexity into actionable insights within hours" (Association of Cancer Care Centers, 2024).

AI-discovered drugs in Phase 1 trials achieve 80-90% success rates versus 40-65% for traditionally discovered drugs (Association of Cancer Care Centers, 2024). Yet consumers shouldn't expect lower prices. "The most interesting thing is when you talk to other management teams in other industries about AI, they tend to talk about saving money first," observes Jones. "When you talk to drug company executives and heads of R&D, they acknowledge there are absolutely some cost savings...but what gets them really excited is developing drugs that were previously un-druggable targets."

Un-druggable targets are disease-causing molecules researchers understand but cannot effectively target with medications (American Chemical Society, 2024). Cost savings get reinvested into research rather than passed to consumers. Jones suggests AI-driven INDs could triple within three years, flowing through to increased approvals and revenue opportunities (MFS Investment Management, 2024).

Winners and Losers: The Data Advantage

AI integration creates clear competitive advantages for data-rich incumbents. "The first thing that's become clear is the advantage that pharmaceutical companies have is all of the data based on all of the drugs they have in their pipeline today or in their base business today and the years of running clinical trials with those drugs," explains Jones. "You can think of pharmaceutical companies as data rich, and the tech companies are rich in AI talent" (MFS Investment Management, 2024).

This spawns strategic partnerships. Roche exemplifies this through its "lab-in-the-loop" methodology, where lab data trains AI models that make predictions, which get tested and generate new data for retraining (Roche, 2024). Companies dominating specific therapeutic areas accumulate unmatched insights, creating what MFS calls a "virtuous cycle."

"A company that really specializes in one therapeutic area that has the world's largest drug in that therapeutic area and has therefore the most patient data and years of clinical data, will be able to use that to potentially create new versions of that drug, combinations with that drug," explains Jones. "Or you think about deploying that drug in new ways to treat other illnesses, and that would be very unique to that one company."

This incumbency advantage challenges startups lacking robust data repositories. "This is going to make the bigger company stronger and the winners keep on winning," concludes Jones.

Investment Implications

MFS's research methodology provides frameworks for identifying AI leaders. "With that framework in mind, what's so interesting about AI is I wanted to ask the question, how could AI change the probability of success for either more new drugs to come through the pipeline or better drugs to come through the pipeline?" reflects Jones. "And one of the best parts about working at MFS is we have access to management teams from every pharmaceutical company around the world" (MFS Investment Management, 2024).

Key evaluation criteria include data depth and quality, strategic AI partnerships with technology leaders, therapeutic area dominance, and management commitment. Currently, only 3-4% of INDs involve AI, but this figure could triple within three years (MFS Investment Management, 2024).

"What excites me as an investor," reflects Jones, "is coming back to where we started, what's the most important thing for a drug analyst to focus on? New drugs. What could change that? AI in drug development. So we're spending a lot of time trying to understand it, trying to dig into it, because if we can understand those trends and increase our probability of identifying those big new drugs as they come to market, we're going to hopefully find stocks that outperform for our clients."

Despite uncertainties, MFS maintains conviction: "AI could mean more innovation. Innovation creates big drugs. And big new drugs drive drug stocks," summarizes one analyst. "It's hard to think of a drug stock that has outperformed without a great new drug driving it."

Success requires identifying companies combining data richness, therapeutic expertise, technology partnerships, and management excellence. "Our job is to roll up our sleeves, understand that, take risk, and dig into this in as much detail as we can to try to identify those winners and own them over very long periods of time and hopefully drive tremendous amounts of alpha for our clients."

References

American Chemical Society. (2024). AI-driven drug discovery: A comprehensive review. ACS Omega. https://pubs.acs.org/doi/10.1021/acsomega.5c00549

Association of Cancer Care Centers. (2024, December 20). Harnessing artificial intelligence in drug discovery and development. https://www.accc-cancer.org/acccbuzz/blog-post-template/accc-buzz/2024/12/20/harnessing-artificial-intelligence-in-drug-discovery-and-development

DrugBank. (2024, August 9). Investment trends in pharmaceutical research. https://blog.drugbank.com/investment-trends-in-pharmaceutical-research/

MFS Investment Management. (2024). 2025 - Six key themes. https://www.mfs.com/en-us/individual-investor/insights/market-insights/key-themes.html

MFS Investment Management. (2024). Investing in pharma & the game changing impact of AI [Audio podcast]. https://www.mfs.com/en-us/investment-professional/mfs-podcasts/sustainable-investing/investing-pharma-ai-impact.html

National Institutes of Health. (2023). Artificial intelligence in drug discovery and development. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC7577280/

National Institutes of Health. (2023). The role of AI in drug discovery: Challenges, opportunities, and strategies. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC10302890/

Roche. (2024). AI and machine learning: Revolutionising drug discovery and transforming patient care. https://www.roche.com/stories/ai-revolutionising-drug-discovery-and-transforming-patient-care

U.S. Food and Drug Administration. (n.d.). Development & approval process | Drugs. https://www.fda.gov/drugs/development-approval-process-drugs

U.S. Food and Drug Administration. (2024). Artificial intelligence for drug development. https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/artificial-intelligence-drug-development

This article is for informational purposes only and should not be construed as investment advice.