Google’s AI for Drug Discovery: A Win for Scientific Research

• Google’s launch of the Cell2Sentence-Scale 27B (C2S-Scale) model, built on the Gemma family of open models, has been successful in suggesting a new drug combination for cancer therapy.

• The model, built on a large dataset of real-world patients and cell-line data, suggested a drug called silmitasertib could improve the immune system’s ability to identify cancerous tumours.

• The model was trained on a large dataset of real-world patients and cell-line data, suggesting a drug could improve the immune system’s ability to identify cancerous tumours.

• The model’s novelty lies in its ability to scan the vast cancer biology literature to suggest a novel use for a drug candidate, a task pharmaceutical companies typically spend billions of dollars and employ highly trained personnel to reveal.

• The results demonstrate that it’s possible to create LLMs that don’t have to be trained on the rules of biological systems. Instead, models can be encouraged to figure the rules out by having their successes’rewarded’ and failures ‘punished’.

• Indian Institute of Science, Bengaluru, mathematics professor Siddhartha Gadgil interpreted the findings as significant, stating that the best AI models in mathematics are currently at the level of a “skilled mathematician but not at genius-human levels.”

• Gadgil cites the International Mathematical Olympiad 2025, where OpenAI, the maker of ChatGPT, had an “experimental reasoning model” that figured out answers to questions that if it were a human, would have won it a gold medal.

Google AI drug discovery is important topic for Civil service exam (WBCS).

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