Conference Day One | Wednesday, October 15
For more information, download the event guide.
8:00 am Check-In & Light Breakfast
8:50 am Chair’s Opening Remarks
Strategies to Obtain High Quality Datasets to Enhance Validation of Models
9:00 am Building Purposeful Datasets to Reduce Model Bias & Improve Predictive Accuracy
Synopsis
- Learn why retrofitting existing experimental data for model training often introduces intrinsic bias that skews outcomes
- Explore strategies for designing data collection protocols explicitly for machine learning, ensuring consistency and reducing noise
- Understand the importance of resource investment in systematic data generation and where to prioritize for maximum impact
9:30 am Enabling Generalizable Models Through Smarter Data Aggregation & Validation Approaches
Synopsis
- Discuss the limitations of model generalizability due to inconsistent data protocols and lack of standardized evaluation
- Review current industry efforts – such as consortia and cross-pharma collaborations, to pool and harmonize data
- Learn how to avoid misleading conclusions by adopting statistically rigorous model performance assessments
NEW COMPANY!
10:00 am Implementing Active Learning Loops to Combat Bias & Improve Model Performance Over Time
Synopsis
- Discover how active and continuous learning systems allow models to improve with each new dataset, reducing initial bias
- Examine the impact of bias from datasets favoring only “good” molecules and how this limits model creativity and exploration
- Understand how integrating real-time lab results into training loops supports model refinement and validation
10:30 am Speed Networking
Synopsis
This session is a great opportunity to introduce yourself to the attendees that you would like to have more in-depth conversations with. This session is the ideal opportunity to get face-to-face time with many of the computational drug experts to establish meaningful business relationships.
11:00 am Morning Break
11:30 am Panel Discussion: The Data Dilemma – Standardizing Datasets to Improve Model Predictability
Synopsis
- What progress has been made in creating high-quality, standardized datasets to train robust predictive models?
- How are industry leaders overcoming data silos and inconsistencies to drive better biologics insights?
- What role do partnerships, consortia, or public databases play in improving interoperability and model performance?
Enabling Design of Complex Biologic Formats to Expand Therapeutic Possibilities
12:15 pm Expanding Predictive Design Beyond Monoclonal Antibodies to Bi- and Multispecific Protein Therapeutic
Synopsis
- Discuss challenges within multi-specific protein therapeutic design and discovery, exploring how computational tools can support the optimization tasks
- Learn about critical data gaps and strategic data generation approaches specifically tailored for multi-specifics
- Examples and case studies demonstrating the application of computational and machine learning models to optimize next-generation multi-specific therapeutics
NEW COMPANY!
12:45 pm Designing & Predicting Properties of Emerging Biologic Formats Using AI & Structural Insight
Synopsis
- Examine the current limitations of predictive tools for formats like VHHs, Fabs, ARC molecules, and RNA conjugates
- Learn how AI can be tailored to support design and developability assessments across diverse biologic architectures
- Encourage greater collaboration between structural biologists, AI developers, and discovery teams to accelerate innovation in this space
1:15 pm Lunch Break & Networking
2:15 pm Leveraging Sample-Based Quantum Diagonalization (SQD) in Medicinal Chemistry: Applications in Biologics Design
Synopsis
- Boost binding affinity accuracy by precisely ranking biologic candidates and minimizing false positives early
- Enhance biologic design with better in silico models that capture key molecular interactions and behaviors
- Improve pharmacokinetic predictions by modeling complex enzyme interactions to reduce toxicity and development risks
2:45 pm Computational Approaches to Enable Challenge GPCR Agonist
Synopsis
- Apply structure-based and AI-driven methods to identify and optimize novel GPCR agonists with improved specificity
- Leverage predictive modeling and AI-based protein design to address limited GPCR structural data
- Accelerate GPCR-targeted drug discovery by integrating virtual screening with ligand-receptor interaction analysis
3:15 pm Panel Discussion: From Hype to Impact – Realizing the Value of AI in Biologics Design
Synopsis
- What are the current limitations and tangible successes in applying AI/ML to biologics discovery and developability?
- How are organizations measuring ROI from computational investments — speed, cost, accuracy, or all three?
- What infrastructure, culture, and talent are needed to effectively scale AI adoption across discovery and development pipelines?