Day One

• Explore why complex, dynamic, and previously inaccessible targets have challenged traditional drug discovery approaches
• Discover how advances in structural biology, biophysics, computational chemistry, and AI are expanding the druggable proteome
• Understand how molecular glues, degraders, and other emerging modalities are reshaping discovery portfolios and required capabilities
• Identify which technologies are driving value today versus those likely to transform discovery over the next decade
• Learn how leading organisations are combining experimental and computational approaches to pursue challenging targets with greater confidence

• Explore the two-way interplay between generative AI and structural biology, leveraging AI to accelerate experimental structure determination while using structural data and metadata to improve predictive models
• Unlock new opportunities for rational drug design, from de novo biologics and novel small-molecule binding sites to improved target and mechanism-of-action identification
• Harness agentic AI to transform structural biology workflows, reducing data processing and acquisition times while streamlining sample preparation and increasing experimental productivity

Join our Speed Networking Session for a series of focused introductions with fellow experts across computational chemistry, structural biology, AI, biophysics, and discovery research. In this dedicated structured networking session, exchange ideas, identify potential collaborators, and build valuable relationships that can accelerate your work long after the conference ends.

• Learn how to design efficient screening and assay cascades integrating pharmacological, biophysical, and early ADME data to progress compounds from primary hit discovery through secondary validation and identify high-quality starting points
• Understand how structural biology and biophysics inform hit discovery strategy, assay selection, and hit triage, ensuring resources are focused on the compounds with the highest likelihood of success

• Leverage phage display, structural biology and Cryo-EM to identify, characterise and down-select antibody leads against novel pan-cancer target onco-fetal chondroitin sulfate
• Integrate target biology, binding characterisation and in vivo localisation studies to prioritise candidates with the greatest translational potential
• Optimise pharmacokinetic profile using structural biology and functional datasets to de-risk candidate selection and increase confidence in progression toward clinical development

• Native mass spectrometry can be utilized as a high-throughput screen which generates a rich and unique dataset during screening campaigns
• Complementary techniques, such as HDX, allow functional hits to be predicted  by monitoring changes in conformational dynamics. These predictions show good  translation into established cellular assays
• The combination of data provides a deeper understanding of structure-activity relationships ahead of initiating medicinal chemistry campaigns

• Introducing sphereOME: Leveraging deep proteomic and interactomic profiling of nearly 10,000 molecular glue degraders to map the accessible CRBN neosubstrate space across 11,000 proteins
• Harnessing sphereAI for rational degrader design: Learning the chemical features that drive selective degradation and predicting degradation potential for compound-target pairs, including targets without known degron motifs
• Closing the design loop with AI-driven optimization: Using reinforcement learning to generate novel degrader candidates that balance on-target efficacy with minimized off-target risk and improved selectivity profiles

• Leverage structural modelling to identify optimal antibody conjugation sites, improving ADC stability, payload delivery, and therapeutic performance while reducing empirical design cycles
• Integrate computational modelling and bioinformatics workflows to guide ADC design decisions, accelerate candidate selection, and increase confidence in lead optimisation strategies
• Demonstrate how rational, site-specific ADC engineering translates into improved efficacy and therapeutic index through real-world case studies linking structural insights to preclinical outcomes

The scientific poster session is your prime opportunity to showcase your discovery research and benchmark your results against the attending European discovery community. Make sure to use this time to visit the poster displays, speak to authors, and ask questions to collaborate and uncover the cutting edge of drug discovery innovation.

• Establish rigorous validation frameworks that identify the 50% of AI outputs that are hallucinations, ensuring your teams trust and adopt computational tools rather than dismissing them due to poor early experiences
• Learn how to benchmark AI predictions against experimental data, understanding which use cases deliver value and which remain aspirational
• Discover strategies to educate scientific teams on AI capabilities and limitations, setting realistic expectations and driving productive adoption while avoiding the “magic bullet” trap

• Demonstrate how high-throughput cryo-EM captures transient protein conformations and protein-protein interactions in solution to reveal cryptic and previously inaccessible druggable pockets
• Integrate structural visualization with computational design workflows to identify, optimize, and experimentally validate novel molecules targeting rare functional protein states
• Explore how iterative structure generation and validation can create differentiated datasets to power next-generation AI models capable of predicting protein dynamics and accelerating rational drug discovery

Close out Day One with an open, peer-led discussion exploring the breakthroughs, bottlenecks, and opportunities shaping the future of rational drug design. Exchange perspectives with fellow experts, challenge assumptions, and uncover actionable ideas that can help accelerate smarter, faster, and more confident drug discovery.

• Debate the relative impact of AI, structural biology, biophysics, medicinal chemistry, and computational modelling on accelerating discovery success
• Share lessons learned from integrating experimental and computational approaches to improve confidence in drug design decisions
• Identify the biggest barriers preventing organisations from fully realising the value of next-generation drug discovery technologies
• Discuss which capabilities, platforms, and skillsets teams should prioritise to stay competitive over the next five years