# 🔬Causal Models Need Causal Data - Xaira’s X-Cell model for Drug Discovery (Bo Wang & Ci Chu, Chief Discovery Officer & Chief AI Scientist)

Page: https://stenobird.com/podcast/latent-space-ai-engineer/causal-models-need-causal-data-xaira-s-x-cell-model-for-drug-discovery-bo-wang-ci-chu-chief-discovery-officer-chief-ai-scientist
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Podcast: [Latent Space: The AI Engineer Podcast](https://stenobird.com/podcast/latent-space-ai-engineer)
Published: 2026-07-21T19:34:06+00:00
Episode link: https://www.latent.space/p/xaira
Audio file: https://api.substack.com/feed/podcast/207941607/06ca3e4bd112d187109d3b25b2c9d8fc.mp3
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Duration seconds: 5387

## Resource

Scaling AI for drug discovery requires moving beyond parameter count to information-rich causal data. Xaira Therapeutics' X-Cell model demonstrates that integrating high-throughput perturbation data allows models to predict how unseen cell lines respond to interventions.

## Highlights
- Main idea: Scaling laws for biological models are limited by data information content, not just compute or parameters
- Technical shift: Moving from auto-regressive models (like scGPT) to diffusion-based architectures allows for better modeling of high-dimensional gene expression as an 'editing' process
- Practical takeaway: High-throughput experimentation is essential to generate the causal datasets needed to predict gene expression changes after perturbations
- Failure mode: Training on static datasets like CELLxGENE can capture cell states but fails to predict the dynamics of cellular interventions
- Future frontier: The next breakthrough in 'virtual cell' modeling depends on longitudinal sequencing technology that can track the same cell over multiple time points

## Topics

Drug Discovery, Diffusion Language Models, Gene Expression, Single-cell RNA sequencing, Causal Inference, Bioinformatics, Xaira Therapeutics, Virtual Cell Models

## Chapters
- 1:00 — Introduction to Xaira Therapeutics: An introduction to Bo Wang and Ci Chu and their mission to build an AI-driven drug discovery platform.
- 8:00 — The Power of Predictive Models: Discussing the 'wow moment' when models successfully predict responses to unseen cell line perturbations.
- 15:00 — The Shift to Data-Driven Interventions: Exploring how the rise of LLMs has influenced the approach to modeling biological interventions.
- 21:00 — The Necessity of Causal Data: Why large-scale datasets like CELLxGENE are foundational but require causal context for true predictive power.
- 28:00 — Scaling Perturbation Response: The challenges and opportunities in using high-throughput techniques to scale biological data collection.
- 42:00 — Diffusion vs. Auto-regressive Models: A technical deep dive into why diffusion models are superior for modeling gene expression as an iterative refinement process.
- 55:00 — Validating New Biology: Discussing the recent results from the X-Cell preprint and the discovery of new biological insights.

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