GenAI tool can speed up scientific discovery

Researchers from Monash University and Griffith University have developed a generative AI tool that mimics scientists to support and speed up the process of scientific discoveries.

Named LLM4SD (Large Language Model 4 Scientific Discovery) and described in Nature Machine Intelligence, the new AI system is an interactive LLM tool which can carry out the basic steps of scientific research — ie, retrieve useful information from literature and develop hypotheses from data analysis. When asked, the system is also able to provide insights to explain its results — a feature that is not available for many current scientific validation tools.

“Just like ChatGPT writes essays or solves math problems, our LLM4SD tool reads decades of scientific literature and analyses lab data to predict how molecules behave, answering questions like, ‘Can this drug cross the brain’s protective barrier?’ or ‘Will this compound dissolve in water?’” said lead author Yizhen Zheng, a PhD candidate at Monash University.

“Apart from outperforming current validation tools that operate like a ’black box’, this system can explain its analysis process, predictions and results using simple rules, which can help scientists trust and act on its insights.”

LLM4SD was tested with 58 separate research tasks relating to molecular properties across four different scientific domains: physiology, physical chemistry, biophysics and quantum mechanics. It outperformed state-of-the-art scientific tools that are currently used to carry out these tasks; for example, it boosted accuracy by up to 48% in predicting quantum properties critical for materials design.

“Rather than replacing traditional machine learning models, LLM4SD enhances them by synthesising knowledge and generating interpretable explanations,” said lead co-author Jiaxin Ju, a PhD candidate at Griffith University.

“This approach ensures that AI-driven predictions remain reliable and accessible to researchers across different scientific disciplines,” added lead co-author Huan Yee Koh, a PhD candidate at Monash.

Co-author Professor Geoff Webb, also from Monash, said that LLMs can accurately mimic the key scientific discovery skills of synthesising knowledge from the literature and developing hypotheses by interpreting data.

“We are already fully immersed in the age of generative AI and we need to start harnessing this as much as possible to advance science, while ensuring we are developing it ethically,” Webb said.

“This tool has the potential to make the drug discovery process easier, faster and more accurate and become a supercharged research support for scientists in every field all across the world.”

Co-author Professor Shirui Pan, from Griffith University, concluded, “A model like LLM4SD can rapidly synthesise decades of prior knowledge and then turn around to spot new patterns in the data that might not be widely reported.

“We see this as a key development in speeding up research and development processes and beyond.”

The open-source tool is freely available on GitHub.

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