TLDR: EPAM has published a comprehensive framework addressing the critical need for robust data platforms to enable AI in life sciences R&D. The article emphasizes breaking down data silos, leveraging next-generation tools for data readiness, and establishing a foundation for agentic AI to accelerate drug discovery and clinical trials.
In a pivotal discussion on the future of pharmaceutical research and development, EPAM has unveiled a detailed framework for designing data platforms that are essential for harnessing the power of Artificial Intelligence (AI) in life sciences. Published on August 25, 2025, the article, ‘R&D Revolution in Life Sciences: Designing Data Platforms to Enable AI,’ underscores that the integration of AI and data is no longer merely an opportunity but a fundamental necessity for the industry to compress timelines and enhance productivity in drug development.
The core challenge identified by EPAM is not a scarcity of AI models, but rather the pervasive issue of data that fails to meet the FAIR principles (Findable, Accessible, Interoperable, and Reusable). The current landscape is characterized by fragmented, inaccessible, and non-interoperable data, often lacking contextual metadata, which significantly impedes discovery processes.
To overcome this, forward-thinking organizations must prioritize data integration strategies that unify research, clinical, and real-world data across all departments and functions.
EPAM’s recommendations for breaking down data silos include:
Mapping the data ecosystem: Conducting thorough cross-functional audits to pinpoint the location of critical R&D data, from preclinical studies to real-world evidence.
Establishing data interoperability standards: Adopting common, standardized vocabularies such as Clinical Data Interchange Standards Consortium (CDISC), Health Level Seven (HL7), Fast Healthcare Interoperability Resources (FHIR), and ontologies like Semanticscience Integrated Ontology (SIO). This ensures seamless integration across research, clinical, and commercial functions.
Implementing a federated data architecture: Utilizing approaches like data mesh or data virtualization to enable data access without the need for physical relocation.
The article further highlights the importance of leveraging next-generation tools to achieve data readiness. Emerging technologies for data ingestion, transformation, and labeling are crucial for accelerating the path to AI readiness. These include automation in data wrangling, the adoption of data mesh architectures, and the use of synthetic data for training AI models. These advancements are vital for developing scalable and reusable datasets, enabling AI systems to continuously adapt and learn within complex R&D environments.
Key actions in this area, as advised by EPAM, are:
Automating data pipelines: Investing in AI-native data platforms, such as Databricks and Snowflake with machine learning integration, to support automated ingestion and transformation at scale.
Using active learning for labeling: Employing AI-assisted labeling tools that learn from user inputs to expedite annotation and enhance accuracy.
Centralizing metadata management: Deploying data cataloging tools to meticulously track data provenance, quality, and usage across all datasets.
A significant portion of the framework is dedicated to laying the groundwork for ‘Agentic AI’ – autonomous, task-oriented agents capable of generating hypotheses and making executive decisions. This advanced form of AI necessitates a robust and well-orchestrated data foundation that goes beyond mere centralization. It demands the creation of data contexts that are rich, machine-interpretable, traceable, and actionable throughout the entire drug development lifecycle.
EPAM suggests the following to enable Agentic AI:
Developing semantic data layers: Layering ontologies and knowledge graphs over raw data to provide the scientific context necessary for AI agents to reason effectively.
Creating modular data services: Designing reusable APIs and microservices for efficient access to compound libraries, assay results, and trial endpoints.
Piloting with digital scientists: Initiating with closed-loop AI agents in specific, narrow domains, such as synthesis planning or biomarker discovery, to validate the architectural integrity.
Also Read:
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- PlatformCon 2025: Three Pivotal Shifts Towards AI-First Platform Engineering
The ultimate goal is to apply AI across the entire R&D continuum, from initial target identification to the optimization of clinical trials. Achieving this at scale hinges on data platforms that are inherently flexible, interoperable, and governed. R&D executives, CIOs, and CTOs are urged to consider not only how data supports current AI applications but also how it will enable future innovations. The rewards for this strategic shift are substantial: faster discovery, smarter trials, and ultimately, improved patient outcomes.
