Anto Biosciences
Anto is building a foundation model for microbial communities, making the gut microbiome computable for the first time. We predict drug toxicity and efficacy across diverse populations and fix drugs so they work for everyone — solving the hidden root cause of most drug failures. Founded by Arvid (Broad Institute of MIT and Harvard; Nature-published researcher who pioneered quality-aware, goal-directed sparsification) and David (Harvard Medical School Gastroenterology, J&J), second-time founders
Y Combinator
yc-company-directory #150Anto is building a foundation model for microbial communities, making the gut microbiome computable for the first time. We predict drug toxicity and efficacy across diverse populations and fix drugs so they work for everyone — solving the hidden root cause of most drug failures. Founded by Arvid (Broad Institute of MIT and Harvard; Nature-published researcher who pioneered quality-aware, goal-directed sparsification) and David (Harvard Medical School Gastroenterology, J&J), second-time founders
sourceAnto Biosciences is classified as AI infrastructure from source descriptions and fund-directory context. Similar companies below are ranked from offline product facets and meaningful description overlap; category and source affiliation can only strengthen an existing product match. For lookup-created rows, the profile starts as pending enrichment until deeper source collection runs.
Local clusters rerank extracted product concepts and description terms. The generated graph remains the fallback for sparse descriptions: offline reciprocal product-similarity graph: extracted concepts + description terms + bounded category/affiliation boosts. Minimum fallback score: 0; max competitors: 6.
Companies similar to Anto Biosciences
A deterministic local cluster built from offline product facets and meaningful description terms. Category and selected-institution affiliation provide bounded tie-breaks. Open any peer to continue exploring its cluster.
Aquarium Learning
match 60ML models are only as good as the datasets they're trained on, and that means that most improvement to model performance comes from improvement to the quality and diversity of their datasets. Our tooling makes it easy for ML teams to find anomalies + failure patterns in their datasets and fix these problems by editing / adding the right data. So the next time you retrain your model, it just gets better.
shared product terms: model, time, fix, so; same category: AI infrastructure; shared affiliation: Y Combinator
Sonia
match 57We're on a mission to build safe, voice-based AI that makes high quality mental health support accessible to anyone. Founded by a team of MIT and ETH researchers who published papers at top AI conferences like NeurIPS, ICML, EMNLP and more.
shared product terms: found, mit, publish, researcher; shared product theme: health and clinical care; same category: AI infrastructure; shared affiliation: Y Combinator
Eugit Therapeutics
match 57Eugit Therapeutics targets the issue of non-specific drugs that cause toxicity and clinical trial failures, affecting millions with tissue-specific inflammatory diseases. TAGHOME delivers drugs with precision to diseased tissues using T cell receptors, enhancing safety and improving efficacy. Our initial focus is on the 3.1 million U.S. individuals with Inflammatory Bowel Disease, aiming to initiate clinical trials within two years. Cofounded by George Church (Harvard) and funded by Y Combinator
shared product terms: drug, toxicity, efficacy, cause; shared product theme: health and clinical care; same category: AI infrastructure; shared affiliation: Y Combinator
Lemma
match 49Lemma catches the silent, semantic failures your observability tools miss, where your AI agent looks like it worked but didn’t. We scan every trace to surface issues before users complain, identify root causes, and help you fix them without manual digging, so your agents improve over time.
shared product terms: time, fix, so, root; shared affiliation: Y Combinator
Reticular
match 44Reticular is scaling polygenic prediction for embryo selection, helping IVF couples plan their families today while building a platform to unlock cures for complex heritable diseases long-term. John competed Biology Olympiads before spending 4 years at MIT publishing ML/bio research in NeurIPS and Nature. We believe biological models encode far more information than anyone is currently using - our goal is to unlock this potential.
shared product terms: model, mit, nature, publish; same category: AI infrastructure; shared affiliation: Y Combinator
Miso Labs
match 36Miso Labs is building the world’s most emotive foundation models for voice. Our goal is to pass the voice Turing test.
shared product terms: most, foundation, model, goal; same category: AI infrastructure; shared affiliation: Y Combinator