Innate Innovation is helping Bioplatforms Australia (#BPA) analyse the landscape of artificial intelligence and its potential impact on biomolecular sciences in Australia. Our work earlier this year confirmed a shifting sentiment within the BPA community.
After decades of work on first principles and failing to yield accurate predictions of protein structure, the advent of #AlphaFold has shown us that the complex and intricate molecular information of how proteins fold is present in the data. That is, applying large language models to biology can learn and predict how proteins fold. Its advent disrupts fundamental science and industry. Researchers/innovators in early-phase drug discovery can generate and test an order of 10,000 more targets for a similar time and cost, neatly fitting into an existing commercialisation pipeline. The research transitions from first hypothesising a likely target and gathering data to test to choosing the best-performing target from thousands inspired by data.
Hence, BPA first sought an answer to the question: is this sentiment true across the broader biomolecular science community? That is...
Is #generativeAI the 'aha' moment where the biology discipline changes from hypothesis-oriented to engineering-oriented?
Building on some work by Nature (AI and science - what 1,600 researchers think), we consulted researchers and innovators across BPA's #genomics, #proteomics, #metabolomics and #syntheticbiology communities, ranging from professorial academics, early to mid-career academics, professional facility staff, career bioinformaticians, ML & data engineers and computing engineers.
The answer was a unanimous yes!
A few people think generative AI is a fad, but everyone agrees that the nature of the discipline has changed. The community appreciates machine learning but seeks leadership and a connection to how the generative AI era will disrupt the omics domain.
We found:
Approximately 40% stated that they do not have the skills, data, or computing capability to attempt generative AI and first seek the skills,
Another ~44% stated that they have the skills but not the data (which begs whether they genuinely leverage the generative AI), and
Only 16% have sufficient skills and data, where access to computing and tools was the success limitter.
How does the generative AI era affect BPA as the national funder of instrument, digital and data infrastructure for the biomolecular sciences? How does this affect the business model of #researchinfrastructure and the research itself? What are the emerging early wins other than AlphaFold? How does Australian omics research and industry remain at the forefront of a decade of generative AI disruption to fundamental science and scaled-out translation?
Work is needed to accelerate the stakeholder awareness and adoption of generative AI. Stay posted to find out how we're doing it. If you want to contribute your view/sentiment, you can complete this one-minute survey.
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