)
)
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
-
Innovative AI Framework for Predicting Treatment Response and Adverse Events Performance Characterization Across Multiple Model Architectures Biological insights & Interpretability
12:00
-
Sponsored by Ginkgo Bioworks
12:30
-
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
-
This talk presents a systematic framework for applying artificial intelligence to targeted identification in autosomal dominant polycystic kidney disease (ADPKD), using fine‑tuned biological foundatio …
14:40
-
Sponsored by Excelra
15:10
-
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
-
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
-
Learn to reconstruct gene regulatory cascades from transcriptomic data using interpretable AI, even with limited and complex data Explore a practical workflow for network-guided target discovery in hi …
17:10
-
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 …
12:00
12:30
-
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 …
14:00
-
Cellarity is building an agentic AI platform that fundamentally changes how scientists interact with data in discovery. We'll show how autonomous agents orchestrate data access, literature research, a …
14:30
15:00
-
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; …
