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Introducing The Darwin Gödel Machine: AI that improves itself by rewriting its own code sakana.ai/dgm The Darwin Gödel Machine (DGM) is a self-improving agent that can modify its own code. Inspired by evolution, we maintain an expanding lineage of agent variants,

We are excited to announce the launch of our new series of talks for early career scientists, including assistant professors, newly appointed principal investigators, and researchers preparing for faculty positions.

Very interesting! Just applied it to the dataset that was used in a biomedical imaging project for cell classification. Curious what the thinking steps would mean for genomics and proteomics data 🤔

💡IMO one of the best use of AI Scientist is to reanalyze data to find new insights. Introducing #CellVoyager: AI Compbio Agent that makes new discoveries by autonomously analyzing papers/data, which we then validate🚀 New findings on aging, Covid, scRNAseq etc. Open source!

Gattaca is coming into reality...

Excited to unveil Boltz-2, our new model capable not only of predicting structures but also binding affinities! Boltz-2 is the first AI model to approach the performance of FEP simulations while being more than 1000x faster! All open-sourced under MIT license! A thread… 🤗🚀

Researchers spend hours reviewing scientific literature before a #drugdiscovery campaign can begin. 🚨 Just released: NVIDIA Biomedical AI-Q Research Agent streamlines this process using AI-Q and #BioNeMo blueprints, #NVIDIANIM microservices, and GitHub. This multi-agentic

We’re excited to introduce Text-to-LoRA: a Hypernetwork that generates task-specific LLM adapters (LoRAs) based on a text description of the task. Catch our presentation at #ICML2025! Paper: arxiv.org/abs/2506.06105 Code: github.com/SakanaAI/Text-… Biological systems are capable of

Introducing ALE-Bench, ALE-Agent! Towards Automating Long-Horizon Algorithm Engineering for Hard Optimization Problems Blog: sakana.ai/ale-bench/ Paper: arxiv.org/abs/2506.09050 ALE-Bench is a coding benchmark primarily focused on hard optimization (NP-hard) problems. We

(1/n) Since its publication in 2017, PPO has essentially become synonymous with RL. Today, we are excited to provide you with a better alternative - EPO.

Update to our ICML paper on interpreting features in protein language models: We ask human raters to assess the interpretability of SAE feature activations and ESM activations. Human raters found SAE features far more interpretable! x.com/etowah0/status…

Happy to introduce AlphaGenome, Google DeepMind's new AI model for genomics. AlphaGenome offers a comprehensive view of the human non-coding genome by predicting the impact of DNA variations. It will deepen our understanding of disease biology and open new avenues of research.

Say hello to the gemini-cli, a local CLI to help you build and maintain software with 1,000 free Gemini 2.5 Pro requests per day : )

How to build a thriving open source community by writing code like bacteria do 🦠. Bacterial code (genomes) are: - small (each line of code costs energy) - modular (organized into groups of swappable operons) - self-contained (easily "copy paste-able" via horizontal gene

🧬 Excited to open-source Biomni! With just a few lines of code, you can now automate biomedical research with AI agent! We are releasing Biomni A1 (agent) + E1 (env) with 150 specialized tools, 59 databases, and 105 software. E1 is our first attempt at curating the bio-agent

Our work on "Evaluating the representational power of pre-trained DNA language models for regulatory genomics" led by Amber Tang with help from Nirali Somia & Steven Yu is finally published in Genome Biology! Check it out! genomebiology.biomedcentral.com/articles/10.11…

Becoming an RL diehard in the past year and thinking about RL for most of my waking hours inadvertently taught me an important lesson about how to live my own life. One of the big concepts in RL is that you always want to be “on-policy”: instead of mimicking other people’s

Really interesting article by Leash Bio - I think we (I?) should hold our horses thinking drug discovery will be solved by structure prediction as a foundation model Turns out a simple architecture + really scaling binding data = v. v. strong binding affinity predictor