Emerging AI lab assistants revolutionise scientific research

Emerging AI Lab Assistants Revolutionise Scientific Research

Google DeepMind and BioNTech Develop Advanced AI Tools to Aid Scientific Experimentation

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In groundbreaking developments that signal a significant shift in scientific research, Google DeepMind and BioNTech have unveiled advanced AI lab assistants aimed at revolutionising the planning and execution of scientific experiments.

At a Nobel Foundation event, Sir Demis Hassabis, head of Google’s AI division DeepMind, highlighted the transformative impact of artificial intelligence on biology. “Biology is seeing a revolution as a result of AI software,” Hassabis declared. He elaborated on the creation of a specialised AI model intended to serve as a research assistant. This AI is designed to aid scientists in interdisciplinary collaboration and making unexpected connections, which could accelerate discoveries.

Hassabis detailed the future capabilities of DeepMind’s tools, explaining that they could soon suggest and design experiments based on specified hypotheses, offering a probabilistic view of potential success or failure. “We’re working on a science large language model that could be like a research assistant and maybe help you predict the outcome of an experiment,” he said.

In parallel, German pharmaceutical giant BioNTech, in collaboration with its London-based AI subsidiary InstaDeep, announced the development of an AI assistant named Laila. In a recent announcement on Tuesday, the companies revealed that Laila is built on Meta’s open-source Llama 3.1 model and possesses detailed knowledge of biology.

During a live demonstration, Arnu Pretorius, a research scientist at InstaDeep, showcased Laila’s capabilities in automating routine scientific tasks. The AI can handle functions such as the analysis and segmentation of DNA sequences and visualising experimental results. At BioNTech’s Mainz laboratory, scientists demonstrated Laila’s ability to connect to laboratory devices, monitor ongoing experiments, and detect mechanical failures in real-time.

Karim Beguir, chief executive of InstaDeep, stated, “We do not believe that the future is full AI automation any time soon. We see AI agents like Laila as a productivity accelerator that’s going to allow scientists and technicians to spend their limited time on what really matters.”

InstaDeep also presented AI models devised to assist BioNTech in identifying new targets for cancer treatment. This marks the first major presentation of their joint technology since BioNTech acquired InstaDeep in 2023 for up to £500 million. Despite recognising that other companies like Google’s DeepMind could develop similar AI assistants, Beguir emphasised that having InstaDeep’s technology integrated with BioNTech’s biological expertise provides a unique advantage in the pharmaceutical industry.

These advancements are part of a broader trend in which tech companies are investing billions of dollars in AI models and products, with the conviction that these technologies can revolutionise sectors ranging from healthcare to energy and education.

Initially, AI innovation in science has been concentrated on predicting new drug candidates. However, the real-world experimentation required to bring these new treatments to market remains a significant hurdle. The ambition behind AI research assistants is to streamline this process by more effectively planning experiments, for instance, by selecting the most promising experiments from a set of potential options.

Large language models (LLMs), which have the ability to generate text, code, images, and even DNA or molecular sequences based on extensive data sets, are being adapted by companies such as Google and Microsoft to drive scientific breakthroughs. In 2022, DeepMind’s development of AlphaFold—a system capable of predicting the structure of nearly all known proteins—solved a long-standing scientific challenge, substantially reducing the time required for biological discoveries.

Geneticist and Nobel Laureate Paul Nurse shared at the Nobel event in March that his laboratory frequently uses AlphaFold in biochemical experiments. Nurse noted that while AlphaFold’s predictions are not infallible, they are often sufficiently accurate to serve as invaluable tools for researchers.

Following the success of AlphaFold, Hassabis has launched Isomorphic Labs, an AI drugs offshoot, securing partnerships worth up to $3 billion with pharmaceutical companies such as Eli Lilly and Novartis. Nobel Laureate Paul Nurse is also on the advisory board of Isomorphic Labs.

Similarly, Microsoft’s AI4Science division has been leveraging LLMs to expedite scientific discovery. Chris Bishop, director of AI4Science Research at Microsoft, remarked at a research forum that LLMs have the remarkable capacity to function as effective reasoning engines—a characteristic particularly beneficial in scientific research. Bishop detailed a collaboration with the Global Health Drug Discovery Institute to utilise LLMs in the discovery of new molecules aimed at more effectively treating tuberculosis.

As these technologies continue to evolve, the landscape of scientific research may be set for radical transformation, embedding AI deeply into the heart of experimental planning and execution.

Source: Noah Wire Services