AI in Biotech: Taking Stock of a Decade of Progress and the Challenges Ahead

Artificial intelligence has had a profound decade in biotech, advancing from a promising academic endeavor to a critical driver of drug discovery, precision medicine, and genomics. Yet, as we stand in 2024, the potential of AI in biotech is both dazzling and frustratingly unrealized. The tools are smarter, datasets larger, and computational power greater, but many of the same structural barriers that plagued this field a decade ago persist today.

To understand where we are—and where we might go—it’s crucial to take stock of AI's progress in biotech over the past ten years and evaluate how academia and industry must adapt to fully realize its transformative potential.

The Past Decade: Breakthroughs and Limitations

From Protein Folding to Precision Medicine

AI’s role in biotech reached public consciousness in 2020 with DeepMind’s AlphaFold. Its success in predicting protein structures with near-experimental accuracy was hailed as a game-changer, dramatically reducing the time and cost associated with understanding molecular biology. By 2023, AlphaFold’s database had cataloged over 200 million protein structures, a massive boon for basic research and early-stage drug discovery.

However, while AlphaFold transformed protein folding, its impact on actual drug development has been more muted. Understanding a protein’s structure is a piece of the puzzle but doesn’t address the complexities of drug interactions, off-target effects, or clinical scalability. The "last mile" challenge remains significant.

AI and Genomics

Between 2015 and 2024, AI became deeply integrated into genomics, particularly in analyzing next-generation sequencing (NGS) data. Tools such as DeepVariant by Google Health significantly improved the accuracy of variant calling, while AI-driven genome-wide association studies (GWAS) advanced our understanding of disease etiology.

Yet here, too, the promise has outpaced practical applications. Translating genomic insights into actionable therapies remains slow, largely due to the complexity of biological systems and the sheer volume of noisy data generated.

The Rise of AI in Drug Discovery

AI startups like Exscientia captured headlines by using machine learning to identify new drug candidates. By 2024, multiple AI-discovered drugs had entered clinical trials, including Exscientia’s EXS21546 for cancer treatment. While these advancements are promising, the overall impact of AI on clinical pipelines has been incremental rather than revolutionary.

The Structural Challenges Holding AI Back

Data Fragmentation and Quality

One of the most significant issues remains the fragmentation and inconsistency of data. Biotech’s reliance on proprietary datasets, coupled with the lack of standardized data-sharing practices, continues to stifle collaboration. For instance, AlphaFold’s success is built on publicly available structural data, yet similar open-access initiatives are rare in drug discovery pipelines.

In 2024, the European Bioinformatics Institute (EMBL-EBI) and initiatives like the Pistoia Alliance pushed for greater data-sharing standards, but industry adoption remains slow.

The Talent Mismatch

While AI talent has grown exponentially in the past decade, much of it remains concentrated in tech hubs like Silicon Valley, focused on general AI rather than life sciences. Biotech, by contrast, requires interdisciplinary expertise that blends machine learning, biology, and regulatory knowledge—a rare combination.

This talent gap is especially acute in smaller biotech firms, where budgets limit their ability to compete with tech giants for top talent. Efforts to build hybrid training programs, such as the Chan Zuckerberg Biohub’s 2023 curriculum for computational biology, show promise but remain limited in scope.

Where Things Stand

Growing Impact but Modest Outcomes

While AI has transformed early-stage research in biotech, its impact on later stages—clinical development, regulatory approval, and commercialization—remains limited. Many AI tools excel in the “discovery” phase but stumble when faced with the complexity of scaling and integrating into clinical workflows.

Emerging Opportunities

Recent efforts to address systemic barriers offer hope. Open data initiatives, interdisciplinary training programs, and industry-academic collaborations are beginning to bear fruit. For example, MIT’s J-Clinic has launched projects to apply AI in disease diagnostics, demonstrating the potential of focused, cross-sector partnerships.

The Next Decade: A Call for Focus

If the past ten years were about demonstrating AI’s potential in biotech, the next decade must be about execution. To do this, the sector must address three critical areas:

1. Standardizing Data Practices
   Data quality and accessibility are non-negotiable for AI’s success. Industry players must embrace open standards and invest in clean, interoperable datasets.
2. Bridging Talent Gaps
   Biotech must attract and retain polymaths—experts who can navigate AI, biology, and regulatory environments. Building hybrid training pipelines is essential.
3. Regulatory Evolution
   Policymakers need to provide clear, practical guidelines for AI in biotech, ensuring that innovation isn’t hamstrung by uncertainty.

Conclusion

AI’s role in biotech has grown dramatically over the past decade, delivering breakthroughs that would have been unimaginable just a few years ago. Yet the sector still operates below its potential, constrained by data silos, talent shortages, and regulatory ambiguity.

The question is not whether AI can transform biotech but whether we can remove the barriers standing in its way. The past decade has shown us the promise; the next must deliver the reality. For biotech leaders, policymakers, and researchers, the challenge is clear: to leave behind the excitement of possibility and embrace the discipline of execution. Only then will AI truly live up to its potential in biotech.