Why Investing in Biology Is Essential for Sustainable Drug Development and Healthcare Innovation

Investing in Biology: The Key to Sustainable Innovation in Drug Development and Healthcare

Artificial intelligence speeds up drug discovery, but biology is the true test of how well AI-designed therapies will work in patients.

The biotech industry has faced challenging times recently. Investor enthusiasm has cooled, valuations have dropped, and companies have had to tighten their focus. Still, one area continues to draw significant investment: AI’s role in drug discovery. Several AI-first biotech companies, sometimes called “techbios,” have raised large funding rounds despite the downturn. Examples include Isomorphic Labs’ $600 million raise in early 2025 and Xaira Therapeutics’ $1 billion funding late last year.

These companies rely heavily on machine learning and algorithms that analyze existing data to speed up drug discovery. While AI can identify promising drug candidates, clinical trial setbacks have shown that AI alone can’t solve the toughest challenges in drug development.

The Limits of AI: Why Biology Matters

AI can provide faster, cheaper insights with better odds of success, and future AI tools may outperform today’s. But AI’s impact depends on the quality of biological data it learns from. High-quality, well-annotated biological samples aren’t optional—they’re essential for trustworthy, translatable insights.

The techbio sector highlights this issue. Despite hype, many AI-led approaches have struggled to deliver on expectations. History reminds us that shortcuts in medicine often lead to failure or harm, as seen with past drug disasters. More recently, major clinical trial failures of AI-derived drug candidates (such as those from BenevolentAI and Exscientia) highlight the need for real-world validation and quality data.

Jack Scannell, who coined “Eroom’s Law,” pointed out that drug approvals per billion dollars spent on R&D have halved every nine years, partly because the easy problems have been solved. Tackling harder diseases requires new biological insights and models, areas that have been underfunded.

Challenging healthcare issues like neurodegenerative diseases, complex cancers, and rare genetic disorders are biological problems, not just computational ones. Current disease models often fall short, and no amount of machine learning can fix poor-quality input data. In drug discovery, biology defines the rules; AI is the tool. Ignoring this leads to clinical failures, wasted resources, and rising healthcare costs that the industry and society can’t afford.

Synthetic Data vs. Real-World Validation

Another concern is the growing use of synthetic data to train AI models. While generative AI can produce large datasets, synthetic data—especially when generated from other synthetic data—loses accuracy compared to real biological and clinical data. Without grounding in patient biospecimens, diverse clinical records, and real-world outcomes, AI findings risk being promising in theory but ineffective or harmful in practice.

Given the high cost of clinical failure in both dollars and patient lives, AI predictions must be rooted in real-world, validated biology.

Why Investing in Biology Is Essential for Healthcare

The consequences of misusing AI in health extend beyond biotech companies. Healthcare costs are soaring worldwide, straining public budgets and personal finances. In the U.S., healthcare consumes nearly 20% of GDP. Many European and Asian countries face healthcare expenses that outpace economic growth. Emerging markets struggle to build sustainable systems amid growing disease burdens.

Late-stage treatments for cancer and chronic conditions are extremely costly. To make healthcare more accessible and affordable, early intervention, precision prevention, and better disease models are needed. These advances depend on deeper biological insights.

Investing in biology opens doors to better healthcare innovation, improved patient outcomes, and lower costs. It enables the discovery of new biomarkers for early diagnosis, when treatments are more effective. Improved biological data enhances disease models, reducing clinical trial failures and encouraging innovation. It also supports precision medicine, helping shift healthcare from reactive treatment to proactive prevention.

Biology is not a bottleneck for AI to bypass. It is the foundation AI must respect and build upon.

Building a Biology-First, AI-Enabled Future

AI’s role in drug discovery is undeniable, but only biology can determine how well AI-designed therapies will work in patients. The way forward involves creating systems biology models that reflect the human body’s true complexity—not just isolated cells or DNA segments.

Multiomics approaches—covering genomics, transcriptomics, proteomics, and metabolomics—are vital to capture biology at every level. Drug discovery should be grounded in diverse biobanks and real-world patient data, not just simulations. Including clinical context like physician notes and longitudinal health records helps bridge molecular science with patient experience.

Those who invest wisely in biology alongside AI will lead the next wave of sustainable biotech innovation and help build a healthcare system that is financially viable for the future.

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