Biotech & AI Drug Discovery
AI is compressing the slowest, costliest stage of pharma — early discovery — while gene editing and robotic precision medicine reshape how diseases are treated downstream.
The story
Drug discovery has historically taken 10-15 years and cost billions per approved molecule, with most failures occurring before a candidate reaches patients. A converging stack of protein-structure prediction, generative chemistry, gene editing, and high-throughput sequencing is now attacking each layer of that funnel. The thesis is not that AI replaces wet-lab biology, but that it raises the hit rate and shortens cycle times enough to change the economics of R&D, and that the value accrues unevenly — toward picks-and-shovels suppliers and incumbents with proprietary data and distribution before it reaches platform-only upstarts.
The outlook to 2030
Through 2030, expect AI-designed candidates to move from a minority of pipelines toward standard practice, with regulators (FDA) actively pulling the industry toward computational and human-relevant testing methods. Gene editing should broaden from a single approved therapy toward in vivo and additional indications, while sequencing costs keep falling. The likely winners diverge: scaled incumbents and tools/CDMO suppliers monetize the volume, while pure-play AI platforms must prove clinical and commercial validation, not just faster preclinical timelines.
What is moving the capital
The forces routing money into this theme right now.
FDA roadmap to reduce animal testing
In April 2025 the FDA published a roadmap to make animal studies the exception rather than the default in preclinical safety testing within three to five years, formally elevating AI/in-silico New Approach Methodologies and reaching its stated Year 1 goals.
AlphaFold 3 expands structure prediction to drug interactions
AlphaFold 3 (Nature, May 2024) predicts proteins together with DNA, RNA and ligands at least 50% more accurately than prior methods, with AlphaFold now used by over 3 million researchers across 190+ countries as of late 2025.
GLP-1 obesity/diabetes franchise scale
Eli Lilly's Mounjaro and Zepbound generated combined 2025 sales of about $36.5 billion, roughly 56% of total revenue, funding an R&D budget that increasingly incorporates AI-enabled discovery.
First CRISPR medicine ramps commercially
Casgevy, the only approved CRISPR-based medicine, reached about $116 million in full-year 2025 sales with a Q4 record of $54.3 million, providing the first real-world commercial proof point for gene editing.
The leaders
Structural large-cap anchors — lower-variance exposure to the theme.
Eli Lilly is a diversified pharma leader and the structural anchor of the Biotech & AI Drug Discovery theme, leveraging AI to enhance its pipeline and maintain a robust growth trajectory. Lilly's strategic investments and diversified portfolio offer stability in a high-growth sector.
Why the excitement: Lilly's Q1 2026 earnings call highlighted a 56% revenue increase compared to Q1 2025, driven by key products and strategic investments.
The honest risk: Despite strong growth, increased R&D expenses, up 28%, could pressure margins if pipeline candidates fail to materialize.
Intuitive Surgical is a profitable compounder increasingly integrating AI into surgical procedures. Its established robotic surgery platform provides a strong foundation for future AI-driven advancements.
Why the excitement: Intuitive Surgical's Q1 FY2026 earnings call highlighted a solid start to the year, driven by 17% total procedure growth and broad-based adoption across da Vinci and Ion.
The honest risk: Procedure growth in Japan remains below historical levels following fewer system placements in 2025, and the company remains cautious in its outlook for the Japanese market in the short term.
Thermo Fisher provides the essential infrastructure for the burgeoning biotech and AI-driven drug discovery landscape, positioning it as a stable, lower-variance play within a high-growth sector. With a diversified portfolio and strategic acquisitions like Clario, Thermo Fisher is well-placed to capitalize on the increasing demand for research tools and CDMO services.
Why the excitement: Thermo Fisher is raising its full year revenue guidance to a new range of $47.3 billion to $48.1 billion, incorporating the positive impact of Clario and strong Q1 earnings performance.
The honest risk: A muted macro environment in the U.S. could pressure revenue growth in the academic and government end markets.
Asymmetric plays
Smaller names with higher upside and deeper potential drawdowns.
Recursion Pharmaceuticals is a speculative bet on computational R&D, aiming to translate AI advances into meaningful therapies. The platform seeks to identify novel targets and create first-in-class programs.
Why the excitement: Management sees early signals of pipeline progress, with AI unlocking difficult protein targets in immunology and oncology in partnership with Sanofi (Q1 FY2026 transcript).
The honest risk: Despite platform promise, Recursion's high price-to-sales ratio reflects the speculative nature of AI-driven drug discovery and the risk of clinical trial setbacks.
Asymmetry: Large upside if AI-discovered molecules reach the clinic; deep drawdown given pre-profit, binary pipeline risk.
Schrödinger offers a hybrid tools-and-pipeline approach to computational drug discovery, balancing software revenue with the high-risk, high-reward potential of its internal therapeutic programs. The company's transition to hosted software licensing aims for a more predictable financial profile, while its drug discovery collaborations provide upside exposure.
Why the excitement: Schrödinger's Q1 FY2026 ACV growth of 12% signals increasing adoption of its platform, particularly among top pharma clients integrating the software more deeply into their R&D.
The honest risk: Despite disciplined expense management, Schrödinger's net margin of -40.6% highlights the significant ongoing investment required to advance both its software platform and internal drug pipeline, creating downside risk.
Asymmetry: High upside on software adoption and pipeline wins; sharp drawdown on trial setbacks or slow software growth.
CRISPR Therapeutics is a high-risk, high-reward play on the future of curative medicine, now with an approved therapy de-risking its platform. Success hinges on pipeline execution and navigating the evolving landscape of AI-driven drug discovery.
Why the excitement: With an approved therapy, CRISPR Therapeutics has validated its gene-editing platform, paving the way for potential breakthroughs across its deep pipeline.
The honest risk: Despite platform validation, CRISPR Therapeutics faces significant execution risk in bringing its pipeline to market, compounded by the inherent uncertainty of novel gene-editing therapies.
Asymmetry: Large upside on pipeline approvals; deep drawdown on clinical or commercial disappointment.
Inside the theme
The sub-layers and the leaders that anchor each one.
Frequently asked questions
How is AI changing drug discovery?
AI moved from predicting protein folding to designing molecules, turning a slow, expensive search into a computational problem. That can compress R&D timelines and cost, which is the core of the theme.
Big pharma or AI-native biotechs?
Large pharma and device leaders monetize AI-accelerated pipelines today with lower volatility; AI-native platforms like Recursion and Schrodinger carry far higher asymmetry tied to whether their molecules reach the clinic.
What is the picks-and-shovels angle?
Sequencing, lab tools and contract manufacturers sell into every discovery program regardless of which drug wins, making them a lower-variance way to ride the AI-in-biotech trend.
How does the MoonshotScore evaluate this theme?
It weights revenue growth, R&D intensity and cash runway — distinguishing profitable compounders from pre-revenue platforms with binary pipeline risk.
What is the main risk?
Biotech is binary: clinical trial failures, regulatory setbacks and long cash-burn timelines can move AI-native names sharply, and drug-pricing policy can pressure the large-caps.
Research sources
- McKinsey & Company — Generative AI in the pharmaceutical industry: Moving from hype to reality
- Google DeepMind — AlphaFold 3 predicts the structure and interactions of all of life's molecules
- Nature — Alphafold 3.0: the AI protein predictor gets an upgrade
- U.S. Food and Drug Administration — FDA Achieves Year 1 Goals in Reducing Animal Testing in Drug Development
- U.S. Food and Drug Administration — Roadmap to Reducing Animal Testing in Preclinical Safety Studies
- Eli Lilly and Company — Lilly reports fourth-quarter 2025 financial results and provides 2026 guidance
- CNBC — Eli Lilly (LLY) earnings Q4 2025
- Precision Medicine Online — Vertex, CRISPR Therapeutics Record $115.8M in 2025 Casgevy Sales
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Educational content, not investment advice. Past performance does not guarantee future results.