From fragmentation to fusion: How AI is unlocking the value of multi-omic data in drug discovery

In drug discovery, data is the key to success. Although more and more large-scale data is being generated every year, much of it is inaccessible or fragmented. Genomic sequences sit in one silo, proteomic measurements in another, imaging data in a third, and clinical information across even more systems. The result is vast potential locked behind barriers due to inconsistent formats and disconnected workflows. 

As datasets multiply, the challenge is no longer collecting information but connecting it. This blog explores how AI is beginning to solve that problem by integrating multi-omic and clinical data to accelerate discovery and improve trial design.

AI-enabled data integration

Drug discovery depends on integrating multiple data layers, including genomics, transcriptomics, proteomics, metabolomics, microbiomics, imaging, and clinical records. When each source exists in isolation, scientists spend significant time on manual data cleaning, mapping, and harmonization. 

AI is beginning to reduce this friction. Machine learning models are being trained to align, standardize, and connect complex datasets with minimal manual effort. Several companies have developed tools for use within precision medicine R&D. For example: 

• Athos Therapeutics has developed a no-code multi-omics platform that supports genomic, transcriptomic, proteomic, microbiomic, and metabolomic workflows in a single interface. Researchers can move from raw data to analysis without programming expertise. This approach has been used to identify several novel targets, including one in inflammatory bowel disease that has reached phase 2 clinical trials.
• Owkin applies a similar concept through federated AI models trained across hospital networks in Europe and the US. The company integrates patient data such as gene expression, pathology images, and spatial transcriptomics to discover biomarkers and match patients to therapies. By training models within hospital systems rather than centralizing the data, Owkin reduces compliance barriers and maintains data privacy.

These platforms demonstrate how automation and standardization can make large, complex datasets usable in routine discovery and translational research.

Predicting treatment response

Beyond integration, AI is improving how researchers predict treatment response and optimize trial populations. Early studies often fail because patient cohorts are biologically heterogeneous. Linking multi-omic and clinical data allows researchers to identify responder subgroups and design more precise molecules earlier in development. For example: 

• Recursion Pharmaceuticals combines high-throughput cellular phenomics with generative chemistry to map molecular structure against cellular and phenotypic response. The company reports that its approach shortens the time from discovery to clinical candidates from about 54 to 32 months, cutting cost to IND by more than half.
• Lantern Pharma uses a complementary strategy. Its platform integrates over 60 billion oncology data points from clinical trials, publications, and internal studies to predict patient response and identify new therapeutic uses. The company reports that its AI-guided process has reduced the time to reach the clinic from the typical four to seven years down to about three, with multiple drug candidates now in trials.
• Owkin has also used AI to combine pathology images and genomic data to identify patients more likely to respond to certain treatments. In partnership with Sanofi, its models are helping prioritize disease indications and patient subtypes for specific drug candidates.

Implications for discovery and development

For the biopharma industry, the message is clear.

• Integrating fragmented data accelerates early discovery and reduces cost.
• Predicting heterogeneity earlier in the process lowers the risk of late-stage trial failure.
• Connecting existing datasets is more valuable than expanding them without context.
• AI platforms make data-driven discovery accessible to more researchers.

In later stage drug development, AI tools are also being used to address friction points in patient recruitment and site activation. 

Conclusion

Data fragmentation has limited progress in drug discovery for decades. AI-driven integration is now starting to change that by connecting genomics, proteomics, imaging, and clinical data into coherent systems. The result is faster discovery, better-defined trial populations, and a clearer path from target to therapy.

The future of drug development will depend less on collecting new data and more on making sense of what already exists. AI is giving researchers the means to do exactly that.

For more insight into how AI is being used to advance R&D discovery, read our whitepaper AI-driven drug discovery in 2025: platforms, pitfalls, and progress.