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

**Ci Chu** (0:00)
Well, it really blew my mind in a way.
So when I saw the model make prediction, just print out the heat map of the gene expression changes, look at the actual raw data, and line up the linear baseline prediction, the ground truth, and the XR prediction altogether. It's visually very clear to see that XR prediction is much more similar to ground truth than the linear baseline.

**Bo Wang** (0:22)
This is a wow moment I was talking about in the beginning.

**Ci Chu** (0:25)
This is the first time that someone can put together, not just one perturbacy, but seven genome-wide perturbation campaigns together. Something that jumped out to us biologists right away is that some of the perturbations are context universal.

**RJ Honicky** (0:41)
Hi, I'm RJ. Haneki, CTO of Miraomics. This is Brandon Anderson, who builds RNA therapeutics at Atomic AI, and this is the Latent Space AI for Science podcast. One of the themes that has run through the podcast is how the lab and experimentation and the real world have probably the biggest impact and have the most relevance to whether something is AI for science or something like B2B SAS.
We're really happy to have in the studio with us today Bo Wang and Ci Chu from Xaira Therapeutics. At Xaira, they're building with a bunch of other people an AI drug discovery platform. They're using high throughput experimentation system to collect very large datasets and then training AI models that can predict the way that your cells in your body will respond to drugs and therapeutics. Really happy to have you, big fan of your work. Why don't you two introduce yourselves to the listeners?

**Bo Wang** (1:50)
Hello, everyone. My name is Bo Wang.
I'm SVP and head of biomedical AI at Xaira Therapeutics. I joined Xaira about eight months ago and before that I was associate professor at the University of Toronto in Canada.

**Ci Chu** (2:03)
And I'm Ci Chu.
My first name is incredibly difficult to pronounce unless you speak Mandarin. So I go by Chu, as in Chewbacca or Pikachu. My favorite fictional character. I'm the SVP of AI Enabled Discovery at Xaira. I joined about more than two years ago when I was still in stealth mode. And here I lead the high throughput biology group, generating the kind of data that will feed our AI models and also think about their applications. Before this, I spent about a decade at the intersection of AI and big data and biology.
Previously, I worked at Incitro, leading the Invitro Discovery Platform there. And before that, I was at Verily, which spun out of Google X.

**SPEAKER_4** (2:48)
OK, so you're at Xaira, the company which is on the Pareto frontier of confusing names and mega rounds.
So Xaira is, I think, kind of came out of stealth a few years ago in this really big org, kind of out of nothing. So I'm curious if you can explain a little bit about what is Xaira's mission, what is their thesis statement, like what is special about Xaira, and kind of where you're going in the future.

**Ci Chu** (3:17)
Yeah, Xaira is an AI-enabled drug discovery company. And at the core of our mission, we're using AI platforms to generate better therapeutics to advance patient care. And so we will be making drugs using different AI capabilities. There are three main AI platforms that we're building here. The first one is Protein Design, work that's been out of our co-founder, Dr. David Baker's group from UW.
A lot of the current generation of protein designers are here in the company. So there, the thinking is to use advanced AI technology to develop molecules against previously undruggable targets. The second AI platform, I guess, we'll spend a lot of time talking about today, is the one that I have been working on for quite some time, and just released a preprint on. That's the Virtual Cell or Foundation Model of Biology work. There, the hope is to build a model to predict biology, exactly like you said, and predict what genes and drug molecules will affect cell biology.
The third piece, which we're beginning to build now, is patient representation models. The goal there is to have AI models that can understand which patients will respond to which therapeutics. So hopefully, together, these platform technology will help us make better drugs faster and with a higher success rate than previous technologies to transform what is used to be artisanal, trial and error in the past, into more and more into an engineering discipline.

**Bo Wang** (4:52)
I think what sets Xaira different is not just the one billion people talking around, but also I think Xaira is one of the very few AI native companies for drug discovery that works from end to end of all sections of drug discovery.