The Bioinformatics CRO Podcast Manos Metzakopian - CellCodex and AI-ready datasets
Sep 30, 2025
In this engaging discussion, Manos Metzakopian, Co-founder and CEO of CellCodex, delves into creating AI-ready biological datasets. He highlights the critical need for reproducible perturbation data to advance drug discovery and causal modeling in biology. Manos explains how AI will accelerate decision-making in target selection and disease modeling. He also addresses the importance of single-cell multi-omics and emphasizes the balance between biological plausibility and predictive accuracy in data generation. His insights into fostering collaborations and ensuring strict quality control throughout the process are invaluable.
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AI Needs AI-Ready Biological Data
- AI can transform drug discovery but requires systematic, reproducible, large-scale biological data as fuel.
- CellCodex aims to deliver AI-ready perturbation datasets to let models move from correlation to causation.
Perturbations Unlock Causal Models
- Observational biological data is abundant but lacks perturbations needed for causal models.
- Without systematic perturbation data, AI cannot move from correlation to causation in biology.
Shorten Decisions With Perturbation Data
- Use perturbation datasets to compress decision timelines from years to weeks or months.
- Prioritize experiments that clarify which targets, mechanisms, and disease models are worth pursuing.