Sharing Evo 2, a new foundation model for biomolecular sciences, now available on NVIDIA BioNeMo. This is a collaboration between the Arc Institute, Stanford, UC Berkeley, UCSF, and NVIDIA. It's significant because it's trained on a massive dataset – nearly 9 trillion nucleotides of DNA, RNA, and protein sequences from across the tree of life.
Key aspects:
🧬 Genomic Scale: Trained on an enormous dataset covering diverse species. 🔬 Multimodal: Understands DNA, RNA, and protein sequences. 🧠 Long Context: Can process sequences up to 1 million nucleotides at once. 🚀 Powerful Architecture: Uses a "StripedHyena 2" architecture for efficiency. ✅ Open Components: Key parts, including fine-tuning, are available via the open-source NVIDIA BioNeMo Framework. 🔓Available as NVIDIA NIM microservice.
They've already shown it can predict the effects of gene mutations with high accuracy, and even design functional CRISPR-Cas systems. It's a powerful tool for anyone working with biological sequence data.
So, while AlphaFold primarily predicted existing structures, Evo 2 opens the door to designing entirely new biological sequences for things like drug discovery, agriculture, and materials science. What new possibilities does this unlock?
Congratulations on the release of Evo 2! This model represents a significant advancement in genomic understanding. How do you ensure the accuracy and reliability of insights across the diverse species covered by the model?
Congratulations on the launch of Evo 2! This groundbreaking model has the potential to revolutionize multiple fields, from drug discovery to agricultural innovation. The ability to design novel biological sequences is a game-changer for the entire scientific community. I'm excited to see the amazing impact it will have in research and industry. 🚀
Congratulations on the launch of Evo 2! This is a significant advancement in genomic research. How does the model handle the complexities of genomic data variability across different species to ensure accurate and reliable insights?
This is massive. A model that not only interprets but designs new biological sequences could push biotech into entirely new territory. The ability to process million-nucleotide sequences means we’re looking at real potential for breakthroughs in genetic engineering, synthetic biology, and even personalized medicine. Excited to see how researchers put Evo 2 to work!
Congrats on the launch!
Best wishes and sending lots of wins to the team behind it :)
About Evo 2 on Product Hunt
“A foundation model for genomic understanding”
Evo 2 launched on Product Hunt on February 24th, 2025 and earned 226 upvotes and 7 comments, placing #6 on the daily leaderboard. Evo 2, a powerful biomolecular AI model, provides insights into DNA, RNA and proteins across diverse species.
Evo 2 was featured in Artificial Intelligence (466.2k followers), GitHub (41.2k followers), Tech (621.6k followers) and Science (1.3k followers) on Product Hunt. Together, these topics include over 268k products, making this a competitive space to launch in.
Who hunted Evo 2?
Evo 2 was hunted by Zac Zuo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Evo 2 stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Zac Zuo
Hi everyone!
Sharing Evo 2, a new foundation model for biomolecular sciences, now available on NVIDIA BioNeMo. This is a collaboration between the Arc Institute, Stanford, UC Berkeley, UCSF, and NVIDIA. It's significant because it's trained on a massive dataset – nearly 9 trillion nucleotides of DNA, RNA, and protein sequences from across the tree of life.
Key aspects:
🧬 Genomic Scale: Trained on an enormous dataset covering diverse species.
🔬 Multimodal: Understands DNA, RNA, and protein sequences.
🧠 Long Context: Can process sequences up to 1 million nucleotides at once.
🚀 Powerful Architecture: Uses a "StripedHyena 2" architecture for efficiency.
✅ Open Components: Key parts, including fine-tuning, are available via the open-source NVIDIA BioNeMo Framework.
🔓Available as NVIDIA NIM microservice.
They've already shown it can predict the effects of gene mutations with high accuracy, and even design functional CRISPR-Cas systems. It's a powerful tool for anyone working with biological sequence data.
So, while AlphaFold primarily predicted existing structures, Evo 2 opens the door to designing entirely new biological sequences for things like drug discovery, agriculture, and materials science. What new possibilities does this unlock?