Historically, many breakthrough medicines originated from studying nature. In fact, more than half of all small molecule drugs approved between 1981 and 2019 were derived from or inspired by natural products, according to a review in the Journal of Natural Products. Penicillin originated from mold, the chemotherapy drug paclitaxel from a yew tree, and cyclosporine—the immunosuppressant that enabled organ transplantation—from a soil fungus. These examples illustrate the important, longstanding role of naturally occurring compounds in drug discovery.

If enacted, America’s Living Library Act would do more than catalog biodiversity—it would build a strategic national dataset for AI-enabled biopharmaceutical discovery and help strengthen U.S. innovation leadership.

At a time when biotechnology and AI are reshaping pharmaceuticals, healthcare, agriculture, and industrial production, this bill reflects a growing recognition that biological data constitute a strategic national resource—consistent with assessments from the National Security Commission on Emerging Biotechnology (NSCEB).

Much of Nature's Biosynthetic Potential Is Uncharted

A substantial share of biosynthetic diversity remains uncharacterized due to limited genomic sequencing. A 2024 study in BMC Microbiology analyzed 554 genomes from Paenibacillus—a relatively well-studied bacterial genus—and identified 848 biosynthetic gene clusters (BGCs), 84 percent of which were previously unknown. Because BGCs encode the pathways responsible for producing biologically active compounds—including antibiotics, antifungals, and anticancer agents—this finding highlights the level of possible undiscovered therapeutics.

If similar patterns hold across less-studied organisms found on U.S. public lands, the scale of undiscovered biosynthetic potential is likely significant. Moreover, many BGCs are not expressed under standard laboratory conditions and can only be identified through genome sequencing.

Building the Data Foundation for AI-Enabled Biopharmaceutical Innovation

Modern drug development is increasingly data driven. Advances in protein structure prediction, generative chemistry, and AI-enabled drug design depend on access to large, diverse, and high-quality datasets. The bill’s emphasis on building a standardized, publicly accessible genomic database directly addresses this need.

Recent advances in genomics and AI are expanding the ability to identify and characterize natural products, including through genome-mining tools such as antiSMASH and DeepBGC that can identify BGCs and predict their chemical outputs. However, model performance remains constrained by limitations in the availability and diversity of underlying data, as existing genomic databases are often incomplete and skewed toward more easily accessible organisms.

Expanding sequencing across underexplored ecosystems would increase the range of biological patterns available for model training, improving the ability of AI systems to identify novel compounds and biological mechanisms. U.S. public lands encompass hundreds of millions of acres of preserved ecological diversity, including extreme environments that remain underexplored at the genomic level. Organisms found in these environments may encode novel proteins, enzymes, or metabolic pathways with therapeutic potential. Sequencing and digitizing these genomes would help address current data gaps and strengthen the data infrastructure underpinning AI-enabled drug discovery, synthetic biology, and precision therapeutics.

Biological Data as A Strategic Investment in Innovation Infrastructure

The bill aligns with the White House AI Action Plan’s call to “build world-class scientific datasets” by establishing a whole-genome sequencing program for life on federal lands. It adopts an interagency approach spanning the Department of the Interior, the Smithsonian Institution, HHS, USDA, NIH, NSF, and DOE to coordinate data collection, standardization, and access, producing a national platform for biological data. This coordinated approach helps promote standardization and interoperability—critical for downstream research applications in biopharmaceutical R&D, where reproducibility and data integration are essential.

The total investment—$300 million over five years—is modest relative to the potential returns. Even a single therapeutic breakthrough enabled by expanded genomic datasets could yield substantial health and economic benefits.

Strategic and Global Context

Biotechnology is now an area of intensifying global competition. Leadership in biological data and AI capabilities will shape the next generation of breakthroughs in medicine, agriculture, and advanced manufacturing. The legislation reflects growing international investment in genomic data generation. China, for example, has prioritized large-scale sequencing and data infrastructure development, including through institutions such as the Beijing Genomics Institute. Expanding U.S. capabilities in biological data collection would help sustain U.S. leadership in these areas.

Policy Considerations

• Policymakers should recognize that expanded genomic datasets could reduce key data bottlenecks in AI-enabled drug discovery and support biopharmaceutical innovation.
• Policymakers should ensure that genomic data infrastructure is coordinated, standardized, and interoperable to enable effective use across research domains.
• Policymakers should treat biological data infrastructure as a strategic national asset essential to sustaining U.S. leadership in science, technology, and innovation.