AI-Powered Platforms Are Making Complex Data Usable for Drug Discovery

At its core, Sonrai exists to make complex, multimodal data more usable for our pharma and biotech customers. We’ve built Sonrai Discovery^TM, a cloud-based trusted research environment (TRE) that helps research teams integrate and analyze multiomic, imaging and clinical data – all within one secure and collaborative platform.

AI plays a key role in making fragmented data usable. We use it to integrate multiple data types – from molecular and genomic data to proteomics and pathology images – breaking down silos and enabling researchers to combine these datasets effectively. This integration helps teams accelerate biomarker discovery and precision medicine research.

Reproducibility and data fragmentation are big challenges. Our AI models are trained on millions of data points and validated using verified datasets to ensure accuracy and precision. By standardizing how data is processed and analyzed, we make it easier for teams to reproduce results confidently – which is essential in precision medicine. At the same time, Sonrai enforces fully traceable workflows and version-controlled analysis environments, so teams can confidently audit any result for QC, collaboration, and regulatory submission.

Our TRE is inherently collaborative. Within Sonrai Discovery, teams can create shared workspaces to onboard collaborators across departments or institutions. The platform supports data integration from diverse sources – whether stored on Azure, AWS or HPC (high-performance computing) – and includes built-in tools such as RStudio, Jupyter Notebooks, MLFlow and one-click pipelines. It’s designed so bioinformaticians, clinicians and data scientists can all work toward common goals seamlessly. Cross-functional teams can co-interpret data and boost productivity by reducing reliance on one expert group.

Because all work happens in a transparent, auditable environment, teams can easily generate reports, visualize outcomes and share findings with stakeholders directly within the platform. That traceability builds confidence in the data and speeds up the decision-making process at every level of the organization.

We’re already seeing the emergence of generative AI in drug discovery, but the focus now is on reproducibility and data quality. At Sonrai, we’re working on foundation models that can extract meaningful features from imaging data including whole-slide imaging and immunofluoresence – cutting-edge AI research that relies on feeding models high-quality, well-curated datasets. These models are developed to integrate directly with omics and clinical data.

The more we can standardize and validate these inputs, the more reliable the AI outputs become. Over time, trusted AI pipelines and workflows will become standard practice, leading to faster, more efficient breakthroughs.

Trust remains the biggest barrier. Many researchers still see AI as a black box and want to understand what’s happening behind the scenes. That’s why we use open-source tools and make every step of our process transparent – from data ingestion to output. 

We also focus on education, helping teams new to AI build confidence by seeing how validated, human-in-the-loop workflows lead to trustworthy results.