Explore the Agenda
8:00 am Check-In & Morning Coffee
8:50 am Chair’s Opening Remarks
Leveraging Computational & AI/ML Approaches to Predict & Optimize Leads for Developability Parameters & Identify Liabilities Ahead of CMC & Formulation
9:00 am Unlocking Protein Insights via Seamless Access to AI Models at Scale
- AI breakthroughs are revolutionizing drug discovery and molecular biology
- Research teams face practical barriers including fragmented toolsets, inconsistent infrastructure across cloud and on-premises environments, and complex interfaces hinder adoption by non-computational scientists
- In competitive drug discovery, speed to insight drives success
9:30 am In Silico Developability Assessment for Guided Formulation of Biologics
- Sequence and structure-based in silico modeling enables early, interpretable assessment of key developability liabilities in biologics
- MD simulations refine property predictions by accounting for conformational dynamics and solution-dependent behavior
- Coupling computational predictions with physicochemical and MS data supports guided, risk-informed formulation development
10:00 am Benchmarking Antibody-Antigen Affinity Prediction Across Sequence-, Structure-, & Physics-Based Methods
- Structure-aware approaches outperform sequence-based models, highlighting the importance of explicit 3D epitope–paratope interactions
- PLM and LLM methods underperform overall, indicating limits of sequence-only representations for binding affinity prediction
- Reliable prediction remains driven by structural and physicochemical information, with hybrid approaches showing the most promise
10:30 am Morning Break & Networking
11:00 am Overcoming Challenges in Drug Discovery with Lilly TuneLab
- TuneLab provides access to Lilly-trained AI models to help accelerate breakthrough medicines to patients
- The platform employs federated learning, a privacy-preserving approach that enables biotech developers to tap into Lilly’s AI models without directly exposing their proprietary data
- Hear real-world examples from partners using TuneLab to accelerate their drug discovery work
Translating & Scaling Findings to Develop AI/ML Models for More Complex Biologics
Including Bispecifics, Multispecifics & Conjugated Drug Products
11:30 am Benchmarking AI-Enabled Protein Design Across Complex Biologics Formats: From Function to Manufacturability
- Defining format-aware benchmarking strategies to quantitatively evaluate AI/CDD performance for complex protein therapeutics across function, developability, and manufacturability
- Integrating AI-driven protein design with experimental data generation to optimize for biological activity and CMC-relevant properties across bispecifics, multispecifics, and ADCs
- Addressing nuanced data generation, standardization, and scaling challenges relevant to complex proteins to improve predictive accuracy for stability, aggregation, and functional performance in advanced protein formats
12:00 pm Closed Loop Antibody Engineering: Combining High-Throughput Campaigns with ML for Multi-Objective Design
- Closed loop to static screening: Antibody discovery becomes dramatically more efficient when HTx data and ML are integrated into an iterative, learning system, not a one-off screening pipeline
- Multi-objective is the real bottleneck: Optimizing affinity alone is solved; the challenge (and opportunity) is balancing affinity, specificity, and developability simultaneously, and this requires new modeling and data strategies
- Data quality beats model complexity: The biggest gains come from designing the right experiments (diverse, informative, comparable), not from using more complex ML models
12:30 pm Lunch Break & Networking
1:30 pm Harnessing AI for Smarter Antigen Selection & More Effective TCE Design
- Using ML to integrate diverse datasets and map antigen prevalence across cancer and normal tissues
- Applying the 3T-TRACE platform to assess TCR and TCR-mimetic specificity and off-target cross-reactivity
- Engineering highly specific and safe T-cell engagers against shared targets for effective treatment of solid tumors
2:00 pm Application of AI/ML for Predicting CIEX Binding Differences of mAb Building Blocks
- Sequence pI, the most widely used CIEX binding predictor is a poor predictor of binding to CIEX
- LLM descriptors can be used to predict aCIEX retention time
- 100s of aCIEX data points and their sequence, structure and pLLM based features can be leveraged for selecting and/or designing mAb building blocks
2:30 pm Afternoon Networking & Refreshment Break
Supercharging Molecular Dynamics Approaches with AI/ML Models to Reflect the Folding
Energetics of Biologic Therapeutics
3:00 pm Multiscale Molecular Modeling of mAbs: From Structure to Attributes
- Multiscale modeling enables us to think beyond single structure-based predictions for mAbs
- Bottom-up molecular dynamics approaches could be the key tool that connects molecular structure to its site-specific and bulk attributes
3:30 pm Structure-Guided Computational Insights into Antibody-Drug Conjugates Using Molecular Dynamics Simulations
- In silico predictive modeling of ADC properties
- Importance of MD for ADC design
- ADC design considerations for stability
Moving From Trial & Error to Engineering Predictive Accuracy
4:00 pm Panel Discussion: Building Confidence in AI‑Driven Decisions Across Biologics Design to Derisk CMC & Formulation
As the conference closes, this panel will summarize technical and scientific insights to assess how far the computational and AI/ML space has progressed in biologics design and what must evolve by 2027 to confidently derisk early CMC and formulation decision-making using AI-driven approaches.
- As AI models are increasingly used to generate and prioritize biologics candidates, how much mechanistic or biophysical understanding is needed to advance molecules with confidence, particularly as programs approach early CMC?
- Based on the use cases shared across the last couple of days, where have predictive models clearly enabled better decisions, and where do computational x AI metrics still fall short of delivering scientific or manufacturing confidence?
- At which points do AI-driven insights most often break down between in silico design, wet-lab validation, and early CMC?
- Do we think that confidence scores and model rankings meaningfully reduce experimental burden, or simply reshuffle risk for liabilities like stability, aggregation, viscosity, and immunogenicity?
- How should discovery teams balance speed and exploration with the need for traceability and rigorous scientific justification when progressing biologics candidates?
- Looking to 2027, what evidence will be required for AI to be trusted to prioritize or terminate biologics programs ahead of major early CMC investment?
- Are current limitations in interpretability and confidence temporary, or enduring constraints that will continue to shape AI’s role in biologics R&D?