---
title: "QUICK SPARK: Nvidia-Backed Biotech Says AI Could Make Biology Programmable"
type: "News"
locale: "en"
url: "https://longbridge.com/en/news/296940142.md"
description: "NVIDIA has disclosed a stake in Generate Biomedicines, backing the biotech firm's vision to make biology programmable using AI. Co-founder Gevorg Grigoryan stated that machine learning transforms drug discovery by generating hypotheses at machine scale, combining AI generation with large-scale experimentation to increase clinical success probabilities."
datetime: "2026-08-25T16:16:25.000Z"
locales:
  - [zh-CN](https://longbridge.com/zh-CN/news/296940142.md)
  - [en](https://longbridge.com/en/news/296940142.md)
  - [zh-HK](https://longbridge.com/zh-HK/news/296940142.md)
generator: "portal-rs"
---

# QUICK SPARK: Nvidia-Backed Biotech Says AI Could Make Biology Programmable

**NVIDIA Corp**‘s(NASDAQ:NVDA) AI ambitions extend beyond chips. The company disclosed a stake in **Generate Biomedicines Inc** in its latest Form 13F filing, backing a biotech whose long-term vision is nothing less than making biology programmable.

 **• NVIDIA stock is building positive momentum. Why is NVDA stock trading higher?**

In an exclusive email interview with Benzinga, co-founder and CTO **Gevorg Grigoryan** said the company was founded on the conviction that “machine learning can make biology programmable,” making AI the operating logic behind every stage of its drug discovery platform rather than an add-on technology.

## **From Educated Guesses to Machine-Scale Discovery**

Grigoryan said AI is transforming drug discovery by making scientific hypotheses abundant rather than scarce.

“What is fundamentally different in the age of AI is that hypotheses (educated guesses) become abundant and available at machine scale.”

To capture that opportunity, Generate combines large-scale AI generation with equally large-scale experimentation in what it calls “intentionality at scale.” 

The platform rapidly generates potential drug candidates, validates them through experiments and learns from the results, allowing it to test “orders of magnitude more intentional hypotheses” than traditional approaches.

According to Grigoryan, that continuous cycle ultimately increases the probability of clinical success while helping deliver better medicines to patients.

*Photo*: *Shutterstock*

 **Read Also: EXCLUSIVE: Nvidia's Biggest Contribution to AI Drug Discovery Isn't Money, Generate CTO Says**

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---
> **Disclaimer: This article is for reference only and does not constitute any investment advice.**