One day, Nobel Prize to people for this AI?? LLM! Who are the people in Oncology for this discovery?

AI can help cure cancer like never before! It’s a big thing! AI for human health — what more could an AI application be like?

This is just a beginning, AI can help us cure Cancer! A deadly disease. There is a long way to go, to cure so many diseases, to better our lives, to live to what life can give us.

Demis Hassabis, CEO of DeepMind, was the only name I knew till I researched what they had done and by whom.

In 2024, Hassabis and John M. Jumper were jointly awarded the Nobel Prize in Chemistry for their AI research contributions for protein structure prediction.

Sundar Pichai is another name. But who is the main leader in this research?

The names of other collaborators, such as those from Yale University and Google DeepMind, Google Research are here.

Team, show time now? It’s big news!

Demis Hassabis: The CEO Working to Solve Cancer With AI

As per the research paper published this month, Quotes,

“Cell2Sentence (C2S) framework, which represents scRNA-seq profiles as textual “cell sentences,” to train Large Language Models (LLMs) on a corpus comprising over one billion tokens of transcriptomic data, biological text, and metadata.”

“Scaling the model to 27 billion parameters yields consistent improvements in predictive and generative capabilities and supports advanced downstream tasks that require synthesis of information across multi-cellular contexts”

“Targeted fine-tuning with modern reinforcement learning techniques produces strong performance in perturbation response prediction, natural language interpretation, and complex biological reasoning.”

“This predictive strength directly enabled a dual context virtual screen that uncovered a striking context split for the kinase inhibitor silmitasertib (CX-4945), suggesting its potential as a synergistic, interferon-conditional amplifier of antigen presentation.”

“Experimental validation in human cell models unseen during training confirmed this hypothesis, demonstrating that C2S-Scale can generate biologically grounded, testable discoveries of context-conditioned biology.”

“C2S-Scale unifies transcriptomic and textual data at unprecedented scales, surpassing both specialized single-cell models and general-purpose LLMs to provide a platform for next-generation single-cell analysis and the development of “virtual cells.””

References

Paper here: https://www.biorxiv.org/content/10.1101/2025.04.14.648850v2.full.pdf

Link to data: https://huggingface.co/vandijklab/C2S-Scale-Gemma-2-27B