Blank Bio
Blank Bio is an applied AI research lab focused on increasing the success rates of clinical trials. We do this by training RNA foundation models that learn the patterns that shape disease progression and patient response to treatment. We aim to help pharma make more informed decisions in clinical trials by capturing the biology that makes each patient’s tumour unique. We’re a technical team of AI scientists and engineers from companies including Recursion, Deep Genomics, DeepMind, and Amazon, an
Y Combinator
yc-company-directory #3060Blank Bio is an applied AI research lab focused on increasing the success rates of clinical trials. We do this by training RNA foundation models that learn the patterns that shape disease progression and patient response to treatment. We aim to help pharma make more informed decisions in clinical trials by capturing the biology that makes each patient’s tumour unique. We’re a technical team of AI scientists and engineers from companies including Recursion, Deep Genomics, DeepMind, and Amazon, an
sourceBlank Bio 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 Blank Bio
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.
Strand AI
match 93Strand AI develops foundation models to generate missing bio-data about patients. With this imputed data, pharmaceutical companies can select better patients for their drug trials and shave months from their drug launch timelines. We’ve already trained a multimodal foundation model that integrates spatial biology modalities, beating SOTA at a fraction of the cost.
shared extracted concept terms: foundation, model; shared product terms: bio, trial, train, foundation; shared product theme: health and clinical care; same category: AI infrastructure; shared affiliation: Y Combinator
AbInitio Bio
match 84Abinitio Bio builds foundation models for biomanufacturing, validated against wet lab data. Our first model, Echo, predicts manufacturing outcomes more accurately than standard models, and any pharma or CDMO can adapt it to their own workflow instantly. By making process decisions that take 6-18 months today in hours, we help teams avoid the cost of delay on a single program.
shared extracted concepts: pharma; shared product terms: bio, foundation, model, lab; same category: AI infrastructure; shared affiliation: Y Combinator
Cambridge Cancer Genomics
match 65CCG.ai exist to ensure that each patient has the right drug, at the right time, to beat their cancer. We build the software to enable data-driven precision oncology and systematically develop data-driven biomarkers indicative of treatment response. We believe that increasing amounts of clinical and genomic data have the potential to transform cancer treatment, and enable oncologists to make smarter decisions about which drug to use in which circumstance. We are expanding our team in Cambridge,
shared product terms: increas, clinical, patient, response; shared product theme: health and clinical care; same category: AI infrastructure; shared affiliation: Y Combinator
UrbanKisaan
match 64UrbanKisaan is an AI-for-plant-biology company. We build foundation models and the physical infrastructure that trains them. Shipping a single new seed variety takes about ten+ years. Two on germplasm, three on crosses and selection, three or four on field trials, one on regulatory. Inside that decade, prediction and experiment never properly meet. DNA is abundant, phenotype data is scarce and unpaired, and the cycle never trains itself. We closed that loop. Three foundation models cover the ups
shared extracted concept terms: foundation, model; shared product terms: trial, train, foundation, model; same category: AI infrastructure; shared affiliation: Y Combinator
Exonic
match 64What if you could train a biological foundation model on... the entire internet? Exonic is pioneering a new generation of biological foundation models, focused on heterogeneous, unstructured, and noisy datasets. Our first application is the design of safer gene therapies. In 2025, we used AI to set a new state of the art in liver cancer targeted gene therapy, validated in vitro in our lab in San Francisco. So far in 2026, we have trained a new model with unprecedented zero-shot generalization on
shared extracted concept terms: foundation, model; shared product terms: lab, focus, train, foundation; same category: AI infrastructure; shared affiliation: Y Combinator
AfterQuery
match 64AfterQuery is an applied research lab curating data solutions for frontier foundation model development. Serving every frontier AI lab.
shared extracted concept terms: foundation, model; shared product terms: appli, research, lab, foundation; same category: AI infrastructure; shared affiliation: Y Combinator