Pre-Conference Day

Biophysics offers an abundance of techniques and assays, each with their own benefits and challenges, but knowing which to use, when to use it, and how to evolve your assay strategy across discovery phases is a constant challenge. This interactive workshop brings together early discovery scientists from pharma and biotech to share practical decision-making frameworks for building high-confidence assay cascades.

Join this workshop to:

• Explore practical strategies for designing cascades that evolve with program maturity
• Uncover exclusive insights into trade-offs between throughput, resolution, and confidence in hit identification
• Discuss approaches for choosing between overlapping or orthogonal assays (e.g., SPR vs BLI vs ITC)
• Hear real-world examples from both pharma and biotechs of how assay source (fragment screens, DEL, HTS) and target type influence cascade design
• Extract tips to interpret conflicting data and avoid common pitfalls

Why Take Part?

Leave with practical insights on how to design, adapt, and troubleshoot their own screening workflows according to target class, modality, throughput needs, and data quality.

As biophysics workflows generate increasingly large and complex datasets, AI and machine learning are emerging as transformative tools to enhance signal extraction, automate analysis, and predict experimental outcomes. But how can we integrate these tools effectively, and responsibly, into the experimental pipeline?

This interactive workshop will bring together experts across experimental biophysics, computational modelling, and data science to explore the practical realities, opportunities, and pitfalls of implementing AI in biophysical research and drug discovery.

Join your peers to collaboratively examine:

• How AI and machine learning can streamline data processing, noise reduction, and artefact identification in high-throughput biophysical screening
• Opportunities for predictive modelling to anticipate aggregation, stability, or binding profiles
• What it means to make biophysical data AI-ready: standardization, metadata, and dataset curation
• How to validate AI-driven insights and maintain scientific rigor
• Lessons learned from early adopters applying AI to techniques like SPR, BLI, and NMR

Why Take Part?

Walk away with a practical understanding of where AI integration can genuinely accelerate discovery, and where caution, curation, and collaboration are still required.

Discovery and analytical scientists often use similar biophysical tools, but at different stages and for different goals. This workshop brings both groups together to identify how analytical insights can be applied earlier in discovery, and how discovery-stage decisions can better prepare molecules for downstream development.

This session will cover how cutting-edge biophysical tools and strategies can accelerate molecule characterization, inform developability from aggregation to solubility, and stability, to enhance collaboration across discovery and development teams.

Join your peers to collectively discuss:

• Aligning discovery and analytical objectives when using biophysical data to assess stability, aggregation, and solubility
• Mapping where biophysical data from discovery (e.g., binding, conformation, solubility) can feed into analytical development pipelines
• How analytical parameters like viscosity, aggregation propensity, and high concentration stability can inform molecule engineering before handover
• Examples of organizations integrating analytical scientists into discovery teams, exploring what’s working and what still needs solving
• Emerging techniques (mass photometry, micro-ED, nanoDSF) that are closing the gap between early discovery and formulation insight.

Why Take Part?

Leave with a set of actionable strategies to build cross-functional workflows, accelerate candidate assessment, and use biophysical data to design molecules that are both effective and developable.