AI PLATFORM FOR LIFE SCIENCES

The project is structured around a set of coherent and interdependent objectives, aligned with the four development lines (DL1–DL4) and the implementation activities (A1–A8), ensuring a complete pathway from technological development to validation and market deployment. Strategic and technical objectives: 1. Develop advanced scientific intelligence capabilities (DL1): Enable the automated analysis and synthesis of biomedical knowledge through the integration of scientific literature and domain-specific data sources. This includes supporting evidence aggregation, target identification, drug repurposing, adverse event detection and competitive intelligence, reducing reliance on manual processes and fragmented tools. 2. Develop patent intelligence and freedom-to-operate capabilities (DL2): Support the analysis of complex patent structures, including Markush claims, and enable the identification of potential conflicts between compounds and existing intellectual property. This objective includes the integration of patent data with biological knowledge, allowing more accurate and efficient decision-making in early-stage development. 3. Develop AI-driven clinical trial site selection capabilities (DL3): Enable protocol-specific feasibility analysis through the combination of multi-source data and advanced reasoning models. The objective is to improve the identification of optimal clinical sites based on real-time and contextualised information, increasing efficiency and reducing operational risk in trial execution. 4. Implement federated patient recruitment mechanisms (DL4): Develop privacy-preserving methods for identifying eligible patients across distributed clinical environments. This includes the use of federated data approaches that allow analysis of clinical data without centralisation, improving recruitment efficiency while ensuring compliance with data protection requirements. Integration and system-level objectives: 1. Ensure full integration of all modules into a unified platform (A5): Combine the different development lines into a single interoperable system based on shared knowledge structures, ensuring scalability, consistency and efficient data flow across modules. 2. Develop and deploy advanced AI models and data infrastructure (A2–A4): Structure and integrate heterogeneous data sources, develop core AI capabilities and implement federated infrastructure to support secure and scalable data processing. Validation and operational readiness objectives: 1. Validate the platform in real-world conditions (A6): 2. Perform computational validation and experimental validation in biomedical contexts, as well as testing in clinical environments, ensuring that outputs are scientifically robust and operationally applicable. 3. Ensure technological and operational readiness for deployment: 4. Establish the necessary infrastructure and system performance to support real-world use, including data processing capacity and interaction with clinical environments. Market and implementation objectives: 1. Prepare the platform for market deployment (A7): 2. Define pricing models, engage with pharmaceutical stakeholders and structure the commercialisation pathway, ensuring transition from development to economic exploitation. 3. Ensure effective project management and coordination (A8): 4. Implement governance mechanisms, resource allocation and monitoring processes to guarantee timely execution and alignment between all activities.