Target and Biomarker Discovery (Chair: Leire Escudero-Ibarz, Astra Zeneca)

Advancing Target and Biomarker Discovery: Multiomics Integration and Complex Model Systems in the Age of AI

The convergence of multi-omic technologies, complex disease models, and artificial intelligence is transforming target and biomarker discovery. This session explores how integrated approaches combining molecular profiling with functional validation in physiologically relevant systems enable identification of therapeutic targets and precision medicine biomarkers. Multi-omic profiling provides an unprecedented resolution of disease biology.

However, extracting actionable insights requires advanced integration strategies. AI and machine learning prioritize targets by identifying convergent evidence across molecular layers and patient data, while CRISPR screening in complex model systems enables functional validation of gene function.

This session will showcase disease-agnostic platforms and methodologies that bridge computational power with experimental validation. By demonstrating the synergy between multiomic integration, AI-driven analytics, and scalable functional genomics, the session will highlight how these approaches are accelerating novel therapeutic discoveries, enabling precision medicine strategies, and ultimately improving patient outcomes across diverse therapeutic areas.

Advanced Therapies (Chair: Juan José Lasarte, Universidad de Navarra)

Next-Gen Advanced Therapies: Where AI Meets Synthetic Biology and Immunology

Artificial Intelligence (AI) is revolutionizing the development of new cancer immunotherapies, transforming traditional discovery into a high-precision engineering discipline. By leveraging deep learning and structural bioinformatics, AI allows researchers to navigate the complexity of the immune system with unprecedented accuracy, shifting the paradigm toward highly optimized and personalized therapeutic strategies. A primary application of AI is the design of personalized cancer vaccines. Computational algorithms are now essential for identifying tumor-specific neoantigens and predicting their binding affinity to MHC molecules. This enables the creation of bespoke vaccines tailored to a patient’s unique mutational landscape, maximizing immunogenicity while minimizing off-target effects.

Furthermore, AI is pivotal in the optimization of chimeric receptors for adoptive cell therapies. By refining receptor-antigen interactions and simulating synthetic logic gates, AI helps engineer CAR-T and TCR-T therapies that are more potent and resilient within the immunosuppressive tumor microenvironment. Finally, the design of optimized bispecific antibodies has been significantly accelerated by computational foresight.

This session will address these novel and rapidly evolving fields, which will likely define the development of future, optimized cancer therapies. By integrating computational intelligence into the drug discovery pipeline, we are setting a new standard of care in the global fight against cancer.

Drug Discovery Stories (Chair: Ignacio Sancho, INBISTRA)

Drug Discovery Challenges and Opportunities: Surviving the Gap Between Discovery and the Clinic

Translating a breakthrough into a viable asset requires more than just good science; it demands a strategic “de-risking” mindset. This session explores the evolution of the biotech startup, focusing on the transition from academic innovation to investment-ready pipelines. We will dissect the current landscape of early-stage funding, the strategic utility of non-dilutive vs. dilutive capital, and the necessity of aligning preclinical milestones with clinical value inflection points.

Through a lens of operational discipline, we’ll discuss how founders can move beyond the “business plan” to build a dynamic strategic roadmap that survives market corrections and attracts the right partners. Whether you are navigating your first seed round or preparing for an IND, this session provides the playbook for bridging the gap between the bench and the bank.