An AI sector winner

Artificial intelligence (AI) is often portrayed as a disruptive force. In the pharmaceutical and biotechnology industries, however, it should primarily be understood as a structural value driver, enhancing productivity, improving capital efficiency and strengthening long-term competitive positioning. By compressing timelines in the discovery process and improving efficiency at the clinical and commercial stages, AI-based tools can reduce costs and meaningfully improve industry economics.

The R&D processes of biotech and pharma are inherently data-intensive. Traditional drug discovery involves navigating vast chemical and biological datasets, and typically takes over a decade to complete, costing billions of dollars amid high attrition rates. AI can address these structural inefficiencies directly.

The Pharmaceutical Value Chain

Discovery. By improving target identification, optimising lead compounds and predicting toxicity earlier, AI is expected to reduce failure rates and shorten timelines. For investors, this means greater R&D productivity, reduced sunk costs and more efficient capital allocation.

Machine learning models can detect high-dimensional patterns that are beyond human capability. A landmark example is AlphaFold by DeepMind, which accurately predicted protein structures on a large scale — an achievement recognised with the 2024 Nobel Prize in Chemistry. Computational protein modelling accelerates the discovery of biologics, antibody engineering and enzyme design. AI improves predictions of molecular interactions, reducing the need for trial-and-error experimentation and scientific uncertainty, and expanding the scope for innovative platforms.

R&D data originates from various sources, including genomics, assays, electronic health records, wearable sensors, and scientific literature. AI is highly effective at integrating these datasets on a large scale, providing cross-study insights and enabling multi-omics analysis, which combines genomics, proteomics, and metabolomics to improve our understanding of disease biology. Different processes within discovery require different AI tools. Although these technologies can reduce costs and timelines, the need for proprietary data and limited generalisability across targets remain bottlenecks, meaning that scalability and the realisation of their full transformative potential will likely take time.

Clinical development. The increasing complexity of patient stratification, trial design and response prediction makes AI a particularly valuable tool. Advanced analytics support the prioritisation of portfolios by improving estimates of technical and commercial success. Better biomarker identification and patient selection make trial design more precise, raising success probabilities while reducing delays.

Administrative workflows and reporting can also be streamlined using large language models that have been validated. These efficiency gains improve portfolio IRRs. Since clinical development represents the largest cost component in drug development, even small improvements here can have a significant financial impact.

Financial Impact: Traditional vs. AI-Aided Drug Development

Manufacturing and quality. AI can be used to enhance manufacturing efficiency, supply chain forecasting, pharmacovigilance and quality control. Predictive maintenance, demand modelling and real-time safety monitoring reduce operational risk and protect margins. In a pricing-pressured and highly regulated environment, such efficiencies directly contribute to earnings resilience.

Synergies and deals. AI does not replace scientific or regulatory expertise; rather, it enhances human decision-making. Companies that effectively integrate AI across R&D and operations can gain a lasting competitive advantage, including stronger pipelines, faster innovation cycles and unique data assets. Large pharmaceutical companies are increasingly embedding AI through diversified partnerships. For example, Schrödinger collaborates with Novartis, Lilly, and Otsuka; Recursion has partnered with Roche and Bayer in deals potentially worth billions; and Insilico Medicine works with multiple pharmaceutical companies. Furthermore, companies are increasingly collaborating directly with technology leaders such as Alphabet and Nvidia. A notable example of this is Eli Lilly’s five-year partnership with Nvidia.

Summary. The development of biotech and pharmaceuticals relies on complex datasets. AI enhances our ability to process and act on this data on a large scale, bringing speed, precision and predictive power to processes that were historically incremental. AI augments, rather than replaces, scientific expertise. By reducing development risk and improving capital efficiency, AI strengthens long-term value creation. For investors, the above-described efficiency gains will eventually translate into increased profitability, supporting company valuations and stock price performance.