Virtual cell models promise faster, cheaper early-stage drug discovery. However, the industry still lacks a shared way to judge which of these models can actually be trusted on a given problem. In this episode, Kristóf Szalay, CTO and Co-Founder of Turbine, and Gerold Csendes, Scientist at Turbine, set out what virtual cell models can and can&#x27;t do today, in conversation with host Marilie Fouché. They cover why benchmarking remains fragmented across the field, how pharma teams build confidence in a model before trusting it with real R&amp;D decisions, and how compressing the feedback loop between experiments can cut months out of the discovery process. This episode is sponsored by Turbine. Emerj works with a select group of AI vendors to reach Fortune 500 decision makers through research, media, and direct access. If you want to be considered, download our media kit at emerj.com/AD1

Determining Virtual Cell Impact for Drug Discovery - with Kristóf Szalay and Gerold Csendes of Turbine

The AI in Business Podcast

[SPEAKER_A] Welcome everyone to the Emerj AI in Business Podcast. Today's guest is Kristóf Szalay, CTO and Co-Founder of Turbine, and Gerold Csendes, Scientist at Turbine. Turbine's virtual lab is a software platform that simulates cellular biology at large scale, enabling biopharma teams to run millions of virtual experiments, prioritize wet lab lab work and strengthen preclinical decision packages across discovery programs. Kristóf Szalay and Gerold Csendes join us on today's episode to separate what virtual cell models can reliably do today from where the field is still catching up, and to walk through how pharma teams build enough trust in a model's predictions to change their R&D workflow. Today's episode is sponsored by Turbine. If you offer AI products or services into the enterprise, you need to find enterprise leaders with relevance and readiness. Emerj attracts VP+ enterprise audiences who are already already convinced that they need to move beyond traditional IT. To learn the exact strategies we use to help leading AI brands and startups connect with their ideal enterprise AI buyers, visit emerj.com/AD1. That's emerj.com/AD and the number 1. Now, the conversation with Kristóf and Gerold. </LYRICS> Gerold Kristóf, welcome to the show.

[SPEAKER_C] Hello.

[SPEAKER_B] Thanks for inviting.

[SPEAKER_A] When people talk about virtual cells, the story is often simplified. Build a model that predicts what happens inside a cell when you knock down a gene or add a drug, and suddenly early-stage discovery gets faster, cheaper, and more predictable. It's an exciting idea, and it's getting a lot of attention, but not without controversy. I'd like to slow down the conversation today and look at what these models can claim right now, where the evidence is strong, where it's shaky, and what business leaders should pay attention to if they're evaluating tools in this space....

Determining Virtual Cell Impact for Drug Discovery - with Kristóf Szalay and Gerold Csendes of Turbine

Read the full transcript

The candid version is on a podcast,
not the earnings call.

Determining Virtual Cell Impact for Drug Discovery - with Kristóf Szalay and Gerold Csendes of Turbine

Read the full transcript

The candid version is on a podcast, not the earnings call.

The candid version is on a podcast,
not the earnings call.