Artificial intelligence is reshaping nearly every stage of drug discovery and development. From identifying new therapeutic targets to predicting protein function, designing therapeutics, and improving patient stratification, AI has the potential to accelerate research and bring medicine to patients faster.
But there is one truth that underpins every successful AI application in biopharma: AI is only as good as the data it learns from.
As AI models become more advanced, researchers are discovering that the quality of biological data, not just quantity, is a critical determinant of model performance. High-quality genomic and transcriptomic data provide the foundation for AI to uncover meaningful biological patterns, while incomplete or ambiguous datasets can limit the insights these models generate.
For biopharma organizations looking to harness AI across drug discovery and development, highly accurate long-read sequencing can provide the rich, multidimensional data these models need to generate more meaningful biological insights.
Want to see what this looks like in practice? Join our expert biopharma panel to learn how industry researchers are using HiFi sequencing across drug discovery, biomarker research, gene editing, therapeutic development, vector characterization and more.
AI is driving a new era of multiomic discovery
Today’s AI models are moving beyond single data types. Instead, they increasingly integrate multiomic layers of biological information, including genomes, transcriptomes, proteomes, imaging, and clinical data, to build a more complete picture of disease.
In biopharma, this multiomic approach is enabling researchers to:
- Discover novel therapeutic targets
- Predict the functional impact of genetic variants
- Understand gene regulation and disease pathways
- Design RNA- and gene-based therapeutics
- Identify biomarkers for precision medicine
- Improve patient stratification in clinical trials
With this approach, DNA provides the blueprint, and RNA reveals how that blueprint is being interpreted within cells. Together, they offer complementary insights that allow AI models to better connect genotype to phenotype.
Why high-quality sequencing data matters
Machine learning excels at recognizing patterns, but only when those patterns accurately reflect biology.
Sequencing errors, incomplete genome assemblies, fragmented transcripts, and missing isoforms introduce uncertainty into AI models. Rather than learning biological relationships, algorithms may instead learn artifacts of the underlying data.
This challenge becomes even more significant when studying complex diseases, where subtle genomic variation and transcript diversity can have profound functional consequences.
Reducing uncertainty at the data generation stage allows AI models to focus on discovering biology rather than compensating for missing or inaccurate information.
HiFi sequencing provides the foundation for AI-driven discovery
Highly accurate long-read sequencing enables researchers to characterize regions of the genome that have historically been difficult to resolve.
HiFi sequencing provides highly accurate long reads that help researchers:
- Detect structural variants with confidence
- Resolve repetitive and medically relevant genomic regions
- Phase variants across long haplotypes
- Characterize repeat expansions
- Generate highly accurate de novo genome assemblies
- Better represent genomic diversity across populations
- Capture native DNA methylation information alongside sequence data in every HiFi whole-genome sequencing run, without additional sample preparation or separate assays
This last capability is increasingly important for AI-driven discovery. DNA methylation is one of the most well-studied epigenetic modifications, influencing gene regulation, cellular identity, and disease progression. By generating both genomic sequence and methylation profiles from the same DNA molecules, HiFi sequencing provides a richer, more integrated view of biology that can improve downstream analyses and AI model development.
Rather than training models on genomic data alone, researchers can incorporate both genetic and epigenetic information from a single experiment, enabling AI to uncover relationships between DNA sequence, epigenetic regulation, and disease.
Long-read RNA sequencing adds functional data for AI models
While DNA identifies what is possible, RNA reveals what is actually happening inside cells.
Long-read RNA sequencing enables researchers to observe full-length transcripts directly, providing insights that are often difficult to reconstruct using short-read technologies alone.
HiFi RNA sequencing helps researchers:
- Characterize full-length transcript isoforms
- Detect alternative splicing events
- Identify novel transcripts
- Measure allele-specific expression
- Resolve gene fusions
- Better understand transcript diversity across tissues and disease states
For AI models seeking to understand disease biology, this data provides critical context. Many diseases are driven not only by genomic variation but also by changes in gene expression, RNA processing, and transcript usage.
Capturing these functional layers allows AI to build more biologically meaningful models.
How long-read sequencing strengthens multiomic AI in biopharma
The next generation of AI in biopharma will increasingly rely on integrating multiple layers of biological information, including genomic sequence, epigenomic modifications, transcriptomic profiles, proteomic measurements, spatial biology, and clinical data.
HiFi sequencing contributes several foundational data layers to this ecosystem. In a single DNA sequencing experiment, researchers obtain highly accurate long-read genomic sequences together with native DNA methylation information. Long-read RNA sequencing complements these data by revealing full-length transcript isoforms, alternative splicing patterns, allele-specific expression, and gene fusions.
Together, these complementary datasets allow researchers to connect:
- Genetic variants to changes in gene expression
- DNA methylation patterns to gene regulation
- Alternative splicing to disease mechanisms
- Structural variation to altered transcript function
- Molecular profiles to clinical outcomes
For AI models, these integrated datasets provide a more complete representation of cellular biology. Instead of learning from isolated data types, AI can identify relationships across the genome, epigenome, and transcriptome, leading to more biologically informed predictions and hypotheses.
How long-read sequencing strengthens multiomic AI in biopharma
Highly accurate long-read sequencing does more than improve variant detection or transcript characterization. It increases confidence throughout the AI development pipeline.
By providing a more complete and accurate view of genomic and transcriptomic variation, HiFi sequencing can support more accurate variant interpretation, stronger target identification and biomarker discovery, improved patient stratification, and more robust biological foundation models. Ultimately, richer biological inputs can give researchers greater confidence in AI-driven predictions by allowing models to learn from the biology itself rather than compensate for incomplete observations.
Building the future of AI-enabled biopharma
Artificial intelligence will undoubtedly play a central role in the future of drug discovery. But breakthroughs in AI will depend just as much on advances in biological measurement as they do on advances in machine learning.
HiFi sequencing can provide AI models with multiple complementary layers of high-quality biological information: Highly accurate DNA sequencing captures the complexity of the genome and the ability to sequence full-length transcripts with RNA sequencing reveals how that genome functions in living cells. Together, they provide the high-quality biological data needed to power the next generation of AI applications.
The next breakthroughs in AI-enabled science will come not only from building more powerful models, but from giving those models a more complete view of biology. With richer DNA, epigenetic, and RNA data as their foundation, AI models can help turn biological complexity into discoveries that move biopharma forward.
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