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J&J says AI cuts drug lead development time in half

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Johnson & Johnson says artificial intelligence is reducing by 50% the time needed to generate early drug development leads, accelerating how quickly promising compounds are identified.

According to J&J, AI is helping screen large pools of chemical and biologic candidates faster, improving lead optimization in areas such as cancer and immunology. The company says it has already sped up development for two compounds.

AI is also transforming operations beyond discovery:

  • Clinical trial documentation reduced from hundreds of hours to minutes
  • Faster patient recruitment
  • Improved manufacturing efficiency
  • Enhanced surgical precision in medical devices

J&J says AI is not yet replacing full drug discovery, but it is becoming a major force multiplier in speeding research, regulatory workflows and treatment innovation.

AI Analytics Firm Dataiku Taps Banks for 2026 U.S. IPO Plans

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Artificial intelligence and data analytics startup Dataiku has selected a group of major investment banks, including Morgan Stanley and Citigroup, to lead its long-anticipated initial public offering (IPO) in the United States, according to sources familiar with the matter.

The New York-based company held an internal meeting on Wednesday to officially kick off IPO preparations, with a potential listing targeted for the first half of 2026, the sources said. However, they noted that timing and deal size remain under discussion and could shift depending on market conditions.

Dataiku, founded in 2013, develops software platforms that help enterprises build, test, and deploy AI-driven analytics applications. The company’s tools are used by more than 700 organizations worldwide, including major corporations such as Johnson & Johnson, Toyota, General Electric, and BNP Paribas.

In January 2025, Dataiku said it had surpassed $300 million in annualized recurring revenue (ARR) — a key milestone signaling strong customer retention and subscription growth.

The company was last valued at $3.7 billion following a $200 million Series F funding round in December 2022, led by Wellington Management with participation from existing backers.

An IPO would mark a major step for Dataiku, placing it among a growing wave of AI and software firms looking to capitalize on investor enthusiasm for artificial intelligence. According to Dealogic, 97 companies went public in the third quarter of this year, raising over $24 billion, marking the busiest period for listings since late 2021.

AI-related firms such as Klarna, Figma, and Anthropic have driven renewed momentum in technology listings as markets recover from a two-year IPO drought.

Representatives for Dataiku and Morgan Stanley declined to comment, while Citigroup did not respond to requests for comment.

Analysts say a successful Dataiku listing could further validate investor appetite for AI infrastructure and enterprise analytics companies, which form a critical layer beneath high-profile players like OpenAI and Nvidia.

“Dataiku sits in a sweet spot between enterprise analytics and applied AI,” said one venture capital analyst. “A well-timed IPO could position it as one of the most important public players in AI software beyond model developers.”

If market conditions remain favorable, Dataiku’s IPO could become one of the largest AI software listings of 2026, solidifying its role as a major competitor in the fast-growing enterprise data intelligence market.

Bristol Myers, Takeda Join Forces on AI-Powered Drug Discovery Consortium

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Bristol Myers Squibb, Takeda Pharmaceuticals, and Astex Pharmaceuticals announced a strategic collaboration to share proprietary molecular data for training an artificial intelligence model aimed at accelerating drug discovery and development.

The partnership joins a wider consortium that already includes major pharmaceutical players such as AbbVie and Johnson & Johnson, according to an announcement from Apheris, the German life sciences computing company facilitating the project.

FEDERATED AI FOR DRUG DEVELOPMENT

The companies will contribute data from several thousand experimentally determined protein–small molecule structures to train OpenFold3, an advanced AI model designed to predict protein-ligand interactions — a critical component in developing new medicines.

Unlike traditional data-sharing methods, this collaboration uses Apheris’ federated data platform, which allows multiple organizations to collaborate securely without exposing or transferring sensitive data. Each company’s dataset remains stored in its original location while being used to improve the AI model collectively.

“The federated approach allows us to advance predictive models for small molecule discovery in ways no single organization could achieve alone,” said Payal Sheth, Vice President of Discovery Biotherapeutics and Lead Discovery & Optimization at Bristol Myers Squibb.

A COLLABORATIVE AI STRUCTURAL BIOLOGY INITIATIVE

OpenFold3 serves as the flagship project of the AI Structural Biology Network, an industry-led consortium developed in collaboration with the AlQuraishi Lab at Columbia University. The initiative aims to push the boundaries of AI-driven molecular modeling and create a shared foundation for faster, more efficient therapeutic development.

Hans Bitter, Head of Computational Sciences at Takeda, said the collaboration fits seamlessly into the company’s broader digital strategy.
“This consortium really ties into our larger corporate goal of embedding AI throughout everything we do,” Bitter noted. “It’s a powerful example of how pharma companies can come together to achieve more for patients than we could individually.”

By combining their molecular and structural data through AI, the consortium partners hope to improve drug target identification, binding prediction accuracy, and development timelines, setting a new standard for AI-based biopharma collaboration.