Biotech Startups funded by Y Combinator (YC) 2026

Biotechnology is rewriting life as we know it, from the medicines we take, to the crops we grow, the materials we wear, and the household goods that we rely on every day. Biotech R&D is radically transforming our world, but to move at the new speed of science, scientists need better technology. Benchling’s mission is to unlock the power of biotechnology. The world’s biotech leaders and innovators use our R&D Cloud to power the development of breakthrough products and accelerate time to milestone and market.

Halmos Labs builds automated biotech R&D on the principles of validity and interpretabillity. We run research, and simulations answering open questions ranging from wetlab experiment design to full cancer vaccine manufacturing.

Spectre Intelligence is a neotrading firm. We create AI traders instead of hiring human ones. Currently trading semis + bio.

We’re an AI-native biotech, working on replacing needles with pills. Many drugs, like insulin or Ozempic, are wonderful except you have to inject them. We discover new molecules that you can take as a pill instead. Because the pills operate on biology which has been shown to be effective in people, rather than merely in animal models of disease, we avoid what has historically been the single biggest risk in drug discovery.  This should dramatically increase our probability of success. There’s $200B of treatable diseases and validated human biology without a pill because discovery programs lack a tractable chemical starting point. This is a zero-shot inference problem, because these targets have no training data, so LLMs and other historical interpolative AI approaches are insufficient. Instead, we are building our own extrapolative frontier models that reason beyond their training data. Most critically, we have repeatedly validated our platform on both our own and partner programs. On 4 biologically- and structurally-diverse targets where we had zero training data, we successfully picked binders that were validated experimentally, with confirmation ranging from primary screen hits to crystallographic structures.

We are building robots that can work autonomously in existing wet labs. Unlike traditional lab automation, which requires dedicated equipment and fixed workflows, our robot moves between workstations, manipulates existing scientific equipment, transports lab materials, and executes experiments using the same lab infrastructure as a human technician. We make the robots easy to manage through natural interfaces like Slack, email, and phone. Our goal is to make deploying robots in wet labs as easy as onboarding a new employee, and to accelerate scientific research!

We builds foundational AI models for chemistry. Almost nothing you buy is a single chemical. Products like datacenter coolant, GPU thermal paste, paint, and glue are formulations, which are mixtures of around ten ingredients each, and demand for them has grown quickly because of AI and EVs. Designing a new formulation is essentially a search problem, because there are more possible mixtures than anyone could ever test, and none of them can be predicted in advance. As a result, bringing a single formulation to market today takes three to five years of trial and error in a lab. Our models predict how a mixture will behave far faster than the standard methods used today, which lets us design new formulations in weeks rather than years, at a fraction of the cost. To prove this, we used them to invent three chemicals that never existed before, including a PFAS-free coolant for datacenters, and we have since synthesized and patented all three. We are already working with some of the largest manufacturers to design formulations for them, and we are building our own lab to generate the data that will train our next models.

Most data companies pay for their data. Ours pays for itself. Customers send us a stool sample. We sequence it, build a metabolic model of that gut, and simulate whether each probiotic strain has an open niche it can colonize. We formulate the best candidates, then re-sequence 90 days later to see what actually took hold and what changed. Each cycle gives us the baseline gut, the intervention, and the outcome. At scale, the endgame is models that predict chronic disease years before symptoms.

​Gamgee began with Rosie, founder Paul Conyngham’s dog. After surgery, chemotherapy and immunotherapy failed, Rosie was given months to live. Paul used AI and genomic analysis to identify targets in her tumour, then worked with scientists at UNSW to produce what is believed to be the first fully computationally designed mRNA cancer vaccine delivered to a dog. Following treatment, several tumours shrank significantly and her quality of life improved Gamgee is building an end to end platform for personalised mRNA cancer vaccines for dogs. We sequence a dog’s tumour and healthy DNA, identify mutations unique to its cancer, and design a vaccine that teaches the immune system what to attack. We manage the entire process- from vet intake and tissue collection through genomic analysis, vaccine production, treatment and response monitoring ​We are now turning that one off effort into a repeatable clinical system. Gamgee is running clinical trials in Australia, working with vets, oncologists and specialised laboratories, and accepting cases globally. Our goal is to make personalised cancer treatment accessible to every dog that needs it- and use each case to improve the treatment for the next

Illume is a 24/7 AI personal health & longevity companion that connects your wearables, blood panels, and genomic data to text you personalized and actionable insights. We pull in your health data and fuse it into one secure model. You get daily text updates on what changed, what it means, and what to do next; conversational health Q&A like "What should I do to improve my bloodwork?"; cross-data trend detection that catches patterns individual platforms miss; and a full platform to dig deeper, explore your health data, and text your Illume agent anytime. We're 3 MIT grads & friends, with experience at Windsurf, MIT CSAIL, Eli Lilly, & the Broad Institute.

In an era of cheap intelligence, the most important application of AI will be to improving human health, globally. Osseus provides off-the-shelf or custom medical and biological datasets and RL environments to frontier labs. We work with hospitals and clinics to provide our EHR and deploy AI models within clinical workflows, with models from both our frontier lab partners and our own research.

Molagri develops safer, more effective pesticides for agriculture. We design biopesticides that hit a precise molecular target unique to pest insects, knocking out pests while sparing pollinators, beneficial insects, and the wider ecosystem. By modeling how pests are likely to evolve, we stay a step ahead of resistance, keeping our products effective against the pests of today and tomorrow. The higher efficacy and biobased production will also contribute to emissions reductions in both manufacturing and field use. Our founders are PhD lab mates from the University of Toronto who have worked together for six years and published in Nature Communications and Nature Microbiology.

I run Enjamb. We've worked with 130 life science companies, preclinical through phase 3, to deploy AI securely inside the systems they already work in. Pharma runs on decades of closed software like Benchling, Veeva, Medidata, and SAS. None of it has an AI layer or speaks MCP, so agents cannot reach the systems where the work lives. Enjamb is that layer. Our agents run on top of existing systems and data, with no migration, and they reach the lab systems and internal tools no other AI touches. They learn how a team works, then do the work in Slack, in Teams, and in the systems themselves, across preclinical research, clinical evidence, trial design, statistical programming, and regulatory submissions. This is AI tailored for how medicine gets made.

Describe an experiment in plain English, and Infera turns it into a validated, instrument-ready run across the equipment your lab already uses. Infera handles the protocol logic, vendor-specific scripts, data, inventory, and institutional knowledge in one system. An AI-native compiler for the lab: one system from intent to execution.

10x Science develops frontier AI models with deep memory to redefine how scientists understand and engineer biology across the life sciences, starting with drug development. AI-powered drug discovery is flooding the pipeline with new candidates faster than ever. But before any of them can advance to the clinic, scientists must deeply characterize the protein therapeutic to understand whether it will work. Today, that process takes months of manual, error-prone analysis that cannot keep pace with discovery. Our AI-native platform automates it, giving drug developers the confidence to advance the right candidates and stop the wrong ones early. We have strong commercial traction with enterprise pharma customers. Our founding team built the field of next-generation protein characterization out of Carolyn Bertozzi's Nobel laureate lab at Stanford. Collectively, we have 18+ years of collective domain expertise, 47+ scientific publications, and a 2x YC Founder. We move at the highest velocity and are positioned to lead the next generation of modern AI development in the life sciences.

We’re building AI agents that speed up drug trials by automating the manual consolidation and document prep required for every IND submission. Today, biotechs spend months stitching together reports from contractors and internal teams just to file and get to their first-in-human trial.

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 who published the breakthrough at leading machine learning AI conferences and Nature.

Paceline Bio runs the research and clinical ops layer behind biotech and pharma company who want to move fast, starting with biospecimen collection, logistics, and storage. We're built to get you from scope to signature in just one session so you can accelerate your programs today.

Algen is using CRISPR to uncover disease-driving RNA messages to find treatments for cancer, inflammation and diseases with high unmet needs. We are harnessing the power of CRISPR and machine learning to find first-in-class drugs that modulate RNA messages at single-cell resolution. Algen's founders are bioengineer from Jennifer Doudna Lab and biotech commercialization expert, teamed up with experienced pharmaceutical executives.

We turn unstructured monitoring data - monitoring visit reports, EDC data - into structured deviation, safety, and quality findings for human review. Panoptive generates an audit-ready oversight trail as a natural byproduct. The platform aggregates findings and identifies systemic patterns across sites and subjects before they compound, and tracks every finding through decision, follow-up, and resolution.

AI is making biology easy to engineer. Life-saving drugs and therapies are accelerating, but so is the ability for bad actors to create a pandemic. TwentyTwo is working on the defensive stack to prevent the next pandemic.

An AI-maximalist pharma platform. We use agents to buy drugs, run clinical trials, and sell for a profit. 10x faster and 20x leaner than incumbents. Sourcing Agent: Mines public/private databases and unstructured global data to surface overlooked preclinical candidates. Scientific Agent: Runs comp bio models (ESM-3, RFdiffusion, Boltz-2, AlphaFold) to assess safety and efficacy in silico. Commercial Agent: Analyzes FDA incentives, pricing dynamics, TAM, competitive landscape, and payer alignment. Clinical Agent: Runs digital twin simulations, evaluates CRO/CMC risk, and builds trial plans to Phase 1. PoS Agent: Evaluates the most critical factors that impact the likelihood of clinical trial success. Built by 2 Stanford CS students who have built 3 startups together before.

We make reagents and software that are purpose built for AI, automated labs, and personalized medicines.

Today, it takes months to understand how drugs perform in the real world, delaying safety insights and leaving patients exposed to unknown risks and benefits for far too long. Frekil turns that months-long process into minutes, with full auditability and scientific rigor. Frekil accelerates real world evidence (RWE) generation for life sciences companies on their own clinical data from months to minutes. It connects to fragmented clinical data (EHR, Claims, etc.) and lets teams run end-to-end studies from study design, cohort extraction to SAP, statistical analysis, and final reports.

The intelligence layer behind modern life sciences engagement, enabling compliant, continuous conversations across HCPs, patients, and field teams

Delineate is building AI agents to design better clinical trials faster. This is a $10B dollar market, where saving even 1 day along the process is equal to $1-5M in revenue potential. We process existing clinical trial research into specialized datasets saving scientists months of effort. Delineate is working with two of the largest pharmaceutical companies. With one of our customers, we are helping them get to the next phase of trials faster by creating the largest dataset on a drug class ever constructed.

We use AI and high-throughput mouse studies to discover therapeutics that modify neural activity in the brain. The initial focus is on epilepsies associated with autism and Parkinson’s disease with the potential to expand to any neurological disorder underlined by a dysfunction in neural activity. Our R&D is guided by AI models trained on high-density multimodal mouse data generated in-house. We are 3 Oxford-trained neuroscientists supported by an experienced SAB from Harvard, Science Corp (ex-Neuralink), EPFL, Meta, and UCL. In 1.5 months since landing in SF, we went from nothing to (1) opening and operating an animal lab in South SF, (2) obtaining a proof-of-concept in mice, (3) building a multimodal AI model that directs our screening approach and (4) submitting two provisional patents; all while utilizing less than 50% of our YC funding. 

Reticular 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.

Raycaster is an AI document workspace - think Cursor for regulated content, starting with life sciences teams to draft, edit, and review submissions much faster.

Finding new cures requires seeing what and where the abnormal molecules are in a diseased tissue, but current tools see less than 1% of those molecules. Biocartesian combines microscopy and new chemistries to see 50X more, offering unprecedented insights into disease biology and new therapies.

Kopra Bio makes genetically engineered viruses that teach your immune system to kill cancer using tech we developed at UCSF. We’re making the next Keytruda ($25B/yr cancer drug blockbuster) starting with the most aggressive form of brain cancer, glioblastoma. In the most challenging brain cancer model, we improve survival from 0% with the current FDA approved treatment to 90% with our treatment.

🦠🤖 We use our computational models to make next-generation antibiotics that outcompete bacterial evolution and precisely target pathogenic bacteria, without harming good microbes or human cells. ☠️ Current antibiotics stop working because bacteria evolve resistance to them. This makes drug-resistant bacteria a looming global health crisis - already killing more people than malaria and AIDS and it is getting exponentially worse 📈. 🧬 Our approach leverages co-evolutionary protein-protein interaction datasets combined with AI to forecast bacterial mutations and create ‘future-proof’ antibiotics, addressing antibiotic resistance before it develops. This changes the game for how frequently society will need to make new antibiotics and how long our new antibiotics will be able to treat patients 👩‍⚕️. We are a team of biochemists and evolutionary biologists who met at the University of Oxford.

We are building the next generation of deep-learning models for enzyme design to slash the cost of chemical manufacturing. The $6 trillion chemical industry is flawed: It produces 20% of industrial greenhouse gases, and is responsible for 15% of global energy usage. Enzymes offer a far more sustainable alternative to chemical synthesis and have already revolutionised how a select few chemicals are produced. The problem is each enzyme takes years of trial and error to develop. Our enzyme models learn the principles of catalysis, allowing us to design enzymes for each reaction, in days not years.

ACX is developing a new standard for precision agriculture technologies by providing sustainable, eco-friendly alternatives to chemical pesticides. Our mission is to transform agriculture through sustainable biological crop protection that increases yields while safeguarding the environment, farmers, and consumers.

1849 bio designs microbes enabling cheap metal extraction allowing miners to unlock value from low quality copper and gold ores. Surprisingly, the mining industry is one of the largest scale users of biotech in the world with biomining processes accounting for ~1% of global copper production. Biomining is ultra-low cost, running around ~$1/ton of ore vs ~$7/ton for conventional processes. Unfortunately, while biomining is cheap, it can’t be applied to over 80% of copper ores, leaving vast resources without profitable extraction methods. An estimated ~$800B of copper sit today in waste materials and stockpiles with negative unit economics. While a great deal of effort has been spent on optimizing microbial metal extraction processes, very little effort has been spent on optimizing the microbes themselves. To change that, we’re creating new biotech tools and platforms applied directly to the types of biology most relevant to miners. This enables us to develop new microbes and tackle some of the most difficult problems in biomining, unlocking billions in value from unprofitable resources while being more environmentally friendly than conventional processes. We’re world class microbial engineers. We met while doing our PhDs in synthetic biology, where we spent our time applying and developing the most advanced bioengineering technologies to engineer living cells. 

Angstrom AI builds GenAI-based molecular simulations to substitute wet lab experiments in the pre-clinical drug development pipeline. We are a team of 2 PhD's and 2 Professors from the University of Cambridge who decided to start a company together after we realised how to combine breakthoughs in our research in quantum-accurate models of physics and generative AI models. Our Biotech/Pharma clients can verify the efficacy and safety of new drug candidates using our computer simulations, which match the accuracy of wet lab experiments, but are over 100x faster. We achieve this accuracy by constraining our genAI-based simulations to obey the laws of physics, avoiding the hallucinations seen in other GenAI technologies. Since joining YC, Angstrom AI has developed the first physically accurate gen-AI based simulation of multiple molecules interacting. We have published the first molecule water solubility results with accuracy within the error range of wet lab experiments. We have also kicked-off a 150K pilot project with a pharma company to apply our tech to estimating solubility in their drug development pipeline.

At Undermind, we're building a search engine that can handle extremely complex questions. It’s geared at experts, like research scientists and doctors, who need to find very specific resources to solve high-stakes problems. We’ve rebuilt search from the ground up to address this. Our new approach employs high-quality LLMs to adaptively explore a database, mimicking how a human researcher carefully discovers information. This approach dramatically outperforms (by 10-50x) traditional keyword search and other modern AI-based retrieval methods. Our first target users are the 50 million researchers searching for scientific literature on PubMed and Google Scholar every month. We’ve have paying users across fields like medicine, ML, biotech, finance, and more.

Anthrogen is engineering post-modality biology. Today's modalities reflect historical contingencies in biological progress, not fundamental categories. We develop the AI systems that design modular biological machines and the experimental infrastructure to instantiate them.

We design point-of-care diagnostics for emerging biological threats - in days, not 12+ months.

Granza Bio is developing programmable therapeutics for the immune system. Our platform is built on the foundational discovery of immune “superkiller” attack particles — the natural ammunition used by cytotoxic immune cells to deliver potent, localized killing. By re-arming exhausted immune cells with newly programmed ammunition, Granza restores and enhances immune function. This approach enables a new class of treatments with the potential to transform diseases ranging from cancer to autoimmunity.

SynsoryBio is creating next generation, protein therapeutics that sense where they are in the body and only activate at diseased tissue. This technology platform has the potential to expand the therapeutic window of highly potent drugs and apply to many diseases such as cancer and autoimmune disorders.

ParcelBio is engineering a new class of mRNA medicines designed to deliver unprecedented potency and durability. Its proprietary APEXm™ platform uses novel RNA domains to enhance protein expression and extend activity, enabling therapies that reach the thresholds required for meaningful, and potentially curative, clinical outcomes. The company is advancing programs in autoimmune disease, oncology, and encoded protein therapeutics.

Velorum Therapeutics is developing breakthrough medicines by unlocking the biology of heme.

We are developing cancer therapeutics using small molecules that inhibit immune suppression. Our drugs engage the immune system to aggressively fight tumors. Our therapeutic portfolio addresses a range of solid tumors, starting with mesothelioma.

Tamarind Bio is a website and API which allows scientists to use computational biology tools at scale using a simple interface. On Tamarind, scientists can use ML models like AlphaFold to design and simulate molecules by simply selecting inputs instead of or setting up a high performance computing environment or dealing with DevOps. Tamarind is used by tens of thousands researchers in large pharma companies, top biotechs, and academic institutions.

Gleam automates data entry for clinical research sites.

Pests and pathogens cost the world hundreds of billions of dollars every year. Existing technologies like agrochemicals are no longer working. Ohmic Biosciences uses protein engineering to design resistance genes for crops that are robust to pathogen evolution.

Maven Bio is a live intelligence platform for biopharma teams. It continuously monitors clinical, regulatory, company, and deal developments, identifies what matters to each team's priorities, and delivers signals and updates to keep key documents current. Its monitoring agent can apply edits automatically, with sources attached and the option to roll back changes. Maven runs thousands of monitors across company and individual product websites, trade press, health technology assessment bodies, and patient advocacy groups worldwide. Teams use that intelligence to maintain competitive views, asset screens, and recurring briefs, in Maven or through connected AI assistants and custom workflows.

Nanograb is a computational drug discovery company that uses AI to create novel targeted non-viral gene therapy vectors. Our platform designs unique combinations of small peptide binders attached to nanoparticles to safely deliver genetic medicines to specific cells and tissues in the body at scale.