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This track will explore the latest strategies and tactical insights for how AI/ML is being applied across the discovery pipeline, from data-driven decision-making to target and candidate identification. It will cover generative AI, predictive modelling, and new approaches for scaling beyond proof-of-concept to enterprise-wide deployment and building 'AI-ready' organizations.
11:30
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Innovative AI Framework for Predicting Treatment Response and Adverse Events Performance Characterization Across Multiple Model Architectures Biological insights & Interpretability
12:00
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Sponsored by Ginkgo Bioworks
12:30
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Where has AI made real, measurable impact in discovery — and where has it fallen short? How are scientists and AI systems best complementing each other? How do we define meaningful ROI and decide …Speakers
14:10
14:40
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Sponsored by Excelra
15:10
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Multi-omics profiling has recently emerged as a powerful approach to discover new therapeutics by providing a holistic understanding of systems biology through the integration of various biological mo …
16:10
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How in silico perturbation (ISP) can be used with single-cell foundation models to prioritize driver and rescue genes for target identification in early discovery A biologically grounded evaluation o …
16:40
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Sponsored by Fujifilm
17:10
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Data-driven target prioritization leads to drugs with a higher probability of success in the clinic. Target Engines enable quantitative target evaluation among several criteria. Careful design that …
11:30
12:00
12:30
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Spatial transcriptomics is moving from exploratory studies into clinical-trial decision support (stratification, PD/MoA readouts, response/resistance), but success depends on fit-for-purpose design an …
14:30
15:00
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A Moment of Choice: Takeda’s Lab of Tomorrow reflects a belief that research cannot be incrementally improved; it must be re-imagined. AI, digitalization, and automation are no longer optional tools; …
