A British biotech firm called Basecamp Research has spent the past few years collecting troves of genetic data from microbes living in extreme environments around the world, identifying more than a million species and nearly 10 billion genes new to science. It claims that this massive database of the planet’s biodiversity will help train a “ChatGPT of biology” that will answer questions about life on Earth – but there’s no guarantee this will work. Jörg Overmann at the Leibniz Institute DSMZ in Germany, which houses one of the world’s most diverse collections of microbial cultures, says increasing known genetic sequences is valuable, but may not result in useful findings for things like drug discovery or chemistry without more information about the organisms from which they were collected. “I’m not convinced that in the end the understanding of really novel functions will be accelerated by this brute-force increase in the sequence space,” he says. Recent years have seen researchers develop a number of machine learning models trained to identify patterns and predict relationships amid vast amounts of biological data. The most famous of these is AlphaFold, which can predict the 3D structure of a protein based only on genetic data, and earned its creators at Google DeepMind the 2024 Nobel prize in chemistry.

Full report : British startup, Basecamp Research is collecting a huge databank of genetic data to train its AI.