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AI in Drug Discovery Xchange - Boston - 2026

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  1. Drug Response Prediction
    • Drug Response Prediction
    Poster Presentation: Applying omic data analytics for personalized drug response prediction
  2. Drug Design & Modeling
    • Drug Design & Modeling
    Poster Presentation: Translating Real-World & Multi-Omic Data into Target Prioritization for Drug Discovery
  3. Lead Generation & Optimization
    • Lead Generation & Optimization
    Poster Presentation: Developing drug substances using AI-driven computational solutions with increased efficacy and reduced side effects
  4. Data Quality
    • Data Quality
    Poster Presentation: Integrating Protein Structure Prediction into AI Pipelines: Ensuring Data Quality for Reliable Drug Discovery
  5. Drug Design & Modeling
    • Drug Design & Modeling
    Sponsor Led Roundtable: Variability in preprocessing pipelines reduces confidence in results
  6. Drug Response Prediction
    • Drug Response Prediction
    Sponsor Led Roundtable: Creating Shared Standards for Reproducible Progress in AI Drug Response Prediction
  7. Drug Design & Modeling
    • Drug Design & Modeling
    From model to molecule: Accelerating GPCR drug discovery with AI, novel chemistry, and under explored biology
  8. Lead Generation & Optimization
    • Lead Generation & Optimization
    Harnessing AI-powered de novo design to navigate the hit-to-lead optimization journey (Topic TBC)
    Christopher Wassif
    Christopher Wassif, Senior Director-Emerging Technologies & High-Throughput Platforms - AstraZeneca
  9. Drug Design & Modeling
    • Drug Design & Modeling
    Using AI to model ligand binding to target proteins and affinity predictions
    The latest of the protein-ligand co-folding methods Single vs, multiple ligand poses Need for pose r ...
    Govinda Bhisetti
    Govinda Bhisetti, Vice President, Computational Chemistry - Cellarity
  10. Drug Response Prediction
    • Drug Response Prediction
    Improving in-vivo predictability of AI models trained on in-vitro data (Topic TBC)
    Aridaman Pandit
    Aridaman Pandit, Director II - AbbVie
  11. Target Identification
    • Target Identification
    Poster Presentation: Tracing the history and model tractability of datasets
  12. Drug Response Prediction
    • Drug Response Prediction
    Training AI models on cell line and patient-derived data to improve response prediction
  13. Lead Generation & Optimization
    • Lead Generation & Optimization
    Use of AI to reduce timeline of lead generation to optimization (Topic TBC)
    Donovan Chin
    Donovan Chin, Vice President of Computational Drug Discovery - Parabilis Medicines
    • Data Quality
    • Target Identification
    • Lead Generation & Optimization
    • Drug Response Prediction
    • Drug Design & Modeling
    Drinks-Canape Reception
    • Data Quality
    • Target Identification
    • Lead Generation & Optimization
    • Drug Response Prediction
    • Drug Design & Modeling
    Registration
    • Data Quality
    • Target Identification
    • Lead Generation & Optimization
    • Drug Response Prediction
    • Drug Design & Modeling
    Opening Address & Keynote Presentation:
    Opening Address & Keynote Presentation:
  17. Data Quality
    • Data Quality
    Improving AI outcomes by standardizing data annotation across discovery platforms
    Ankur Patel
    Ankur Patel, Chief Executive Officer - Regenova Pharmacuticals
  18. Target Identification
    • Target Identification
    Enabling drug discovery through high dimensional data: Elucidating novel druggable nodes by connecting disease biology to chemical space through transcriptomics
    Early drug discovery is challenging : Lack of understanding disease biology- lack of new targets Poo ...
    Mauricio Cortes
    Mauricio Cortes, Senior Director Head of Discovery Science - Cellarity
    • Data Quality
    • Target Identification
    • Lead Generation & Optimization
    • Drug Response Prediction
    • Drug Design & Modeling
    1-2-1 Meetings / Networking Break
  20. Data Quality
    • Data Quality
    Accelerating Drug Discovery: AI's role in sifting through scientific knowledge

    Digital Science

  21. Target Identification
    • Target Identification
    Using transcriptomics, GWAS and single cell data together with advanced applied AIML models to uncover novel therapeutic targets

      

    Iulian Pruteanu-Malinici
    Iulian Pruteanu-Malinici, Senior Director, Bioinformatics - Pioneering Medicines
  22. Data Quality
    • Data Quality
    Talking with the Data: Building AI Agent Infrastructure for Collaborative Drug Discovery
    • Data Quality
    • Target Identification
    • Lead Generation & Optimization
    • Drug Response Prediction
    • Drug Design & Modeling
    1-2-1 Meetings / Networking Break
    • Data Quality
    • Target Identification
    • Lead Generation & Optimization
    • Drug Response Prediction
    Morning Refreshments
    • Data Quality
    • Target Identification
    • Lead Generation & Optimization
    • Drug Response Prediction
    Afternoon Refreshments
    • Data Quality
    • Target Identification
    • Lead Generation & Optimization
    • Drug Response Prediction
    • Drug Design & Modeling
    Spotlight Presentation:
    Spotlight Presentation:
    • Data Quality
    • Target Identification
    • Lead Generation & Optimization
    • Drug Response Prediction
    • Drug Design & Modeling
    1-2-1 Meetings / Networking Break
    • Data Quality
    • Target Identification
    • Lead Generation & Optimization
    • Drug Response Prediction
    • Drug Design & Modeling
    1-2-1 Meetings / Networking Break
    • Data Quality
    • Target Identification
    • Lead Generation & Optimization
    • Drug Response Prediction
    • Drug Design & Modeling
    Networking Lunch
  30. Target Identification
    • Target Identification
    Augmented intelligence in drug target identification: separating facts from fables and fiction
  31. Lead Generation & Optimization
    • Lead Generation & Optimization
    Sponsor Led Roundtable Discussion: Breaking the Ground Truth Data Barrier - Using advanced quantum chemistry to provide high-quality training and co-inference data for physics- and AI-enhanced prediction of molecular properties
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