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Google-Backed Isomorphic Labs Delays Clinical Trial Timeline

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Isomorphic Labs, an artificial intelligence-driven drug discovery company backed by Google, now expects to begin its first clinical trials by the end of 2026, marking a delay from its earlier plans, founder and CEO Demis Hassabis said on Tuesday.

Speaking at the World Economic Forum in Davos, Hassabis said the revised timeline reflects the complexity of translating AI-designed drug candidates into human trials. Last year, he had indicated that Isomorphic aimed to have its first AI-developed medicines enter clinical testing by the end of 2025.

Founded in 2021, Isomorphic Labs was spun out of Google DeepMind, which Hassabis also leads. The company applies AI to accelerate drug discovery by improving how potential medicines are designed and evaluated.

Interest in AI-powered drug development has surged as pharmaceutical companies look to shorten research timelines and reduce costs. One of DeepMind’s most prominent achievements, AlphaFold, demonstrated AI’s potential by accurately predicting protein structures, a key challenge in biology.

Isomorphic raised $600 million in its first external funding round last year, led by Thrive Capital, underscoring strong investor confidence despite the longer path to clinical trials.

Schrodinger to Integrate Eli Lilly’s AI Drug Discovery Platform TuneLab

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Biotech software company Schrodinger said on Friday it is collaborating with pharmaceutical giant Eli Lilly to offer Lilly’s artificial intelligence–based drug discovery platform, TuneLab, through its software.

Under the collaboration, Lilly’s TuneLab platform will be integrated into Schrodinger’s cloud-based drug design software LiveDesign, giving biotechnology companies direct access to AI tools designed to accelerate drug discovery and development. Schrodinger said the integration will help researchers move more quickly from early-stage molecule design to viable drug candidates.

LiveDesign is used by chemists to design compounds and predict key properties such as absorption and distribution, helping developers understand how experimental drugs are likely to behave in the body. The addition of TuneLab is expected to further enhance these capabilities by applying AI and machine learning models trained on years of pharmaceutical research data.

Drugmakers and biotech firms have been increasingly adopting AI tools to speed up discovery and safety testing, aiming to reduce costs and development timelines. The trend aligns with efforts by regulators such as the U.S. Food and Drug Administration to encourage alternatives to animal testing in the coming years.

Schrodinger Chief Strategy Officer Karen Akinsanya said existing LiveDesign customers will gain access to TuneLab in the first quarter of this year, while new users will be able to use the AI platform starting in the second quarter.

Eli Lilly launched TuneLab last year to allow external biotech companies to tap into its AI and machine learning models trained on proprietary research data. Lilly has already announced multiple partnerships using the platform to support drug development efforts.

“More biotechs using the models means more diverse training data,” said Aliza Apple, global head of Lilly TuneLab. “Ultimately, this is about moving molecules through discovery faster for the patients who are waiting.”

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.