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"body": "**The problem:** The chronic pain condition Knee osteoarthritis (OA) affects over 250 million people globally, creating significant social and healthcare challenges, with **untreated pain being the #1 patient complaint**. In the absence of effective long-term injectable treatments, millions of patients rely on damaging oral medications. \n\nCapsaicin, offers long-lasting, selective pain relief for chronic pain via molecular ablation of pain-sensing nerves only. Despite FDA approval and proven safety and efficacy, its adoption is limited by **intense burning pain** post-administration, requiring over **1.5 hours of clinic management and making it intolerable for many patients**. This has restricted the use of an otherwise highly effective treatment for chronic pain.\n\n**Our solution:** RM-010 was specifically developed to address the burning pain associated with Capsaicin. Relevium Medical has engineered an innovative drug delivery system that gradually releases Capsaicin into the knee joint, carefully maintaining concentrations **below the threshold that triggers burning pain** while sustaining levels high enough to achieve full nerve ablation. This breakthrough offers patients a highly tolerable, effective, and safe treatment, delivering long-lasting pain relief with a significantly improved experience. RM-010 is designed for seamless integration into clinical workflows, allowing physicians to administer the therapy in **5 minutes**. It provides a practical, patient-friendly solution for effectively managing knee osteoarthritis pain, setting a new standard in treatment care.\n\n\n\n**Who are are:** Relevium Medical, headquartered in Galway, Ireland, is a pioneering pharmaceutical company driven by a passionate team of chemists, neuroscientists, pharmacists, and industry experts. United by a shared mission, the team is dedicated to developing novel solutions to improve the lives of people living with chronic pain.\n\n**Interested in hearing more**: Get in contact at [info@Relevium-Medical.com](mailto:info@Relevium-Medical.com)",
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"body": "Hi everyone,\n\nWe are [**ACX**](https://www.linkedin.com/company/acxtherapeutics/)! We’re two PhD’s in Biochemistry/Biophysics and Computer Science from the University of Cambridge and King’s College London developing a new type of therapy with applications in animal farming and agriculture. We have discovered a new molecular weapon to first provide more efficacious pest control measures.\n\n### **Team:**\n\n👩🏼🔬[**Emmanouela Petsolari**](https://www.linkedin.com/in/emmanouelapetsolari/) – Molecular Geneticist and Biochemist with 8 years of research experience in drug discovery and development (from leukemia, ovarian, and breast cancer as well as viral and bacterial infections).\n\n👩🏼🔬 [**Melina Petsolari**](https://www.linkedin.com/in/melina-petsolari-a03915298/) – Computer Scientist and Designer with extensive experience in HCI, AI, and healthcare developing hardware and software for applications in digital health, biotech, and privacy.\n\n\n\n# **❌ Problem:**\n\nThere is more disease impacting our livestock and crops than ever before - and this comes at a time when the human population is also exploding.\n\n1. Manure from livestock and extensive use of limited therapeutics for growth promotion and disease prevention in animal breeding as well as overuse of pesticides in crops\n\n ● One billion kilos of chemicals are used annually to combat crop pests\n2. Spread of pathogenic agents that have adapted in previously used medications\n3. Failure of commonly used pesticides and insecticides has been documented in nearly 1000 distinct pest species worldwide\n\n ● This leads each year to the world’s food supply facing an up to 30% loss due to insect pests alone, and this number is continuously increasing\n\n# ✅ **Our Solution:**\n\nExisting therapeutics are either no longer effective or have never worked to the extent necessary for achieving positive outcomes. We have discovered a common biological pathway that can be targeted across pathogenic species.\n\n### ✅ ✅ **Specifics:**\n\n1. We have _in vitro_ validation data of our therapeutic showing high inhibition and specificity in the cell line\n2. We are initially focusing on animal health and crop production due to the experimental validation of our therapeutic's effectiveness on a shared pathogen\n3. We are developing drug delivery systems that minimize toxicity and immunogenicity while providing precise spatiotemporal control for targeted drug release\n\n\n\nIllustration of our solution. Nanoparticles carrying the killer compound (orange) infiltrate the target cell like a Trojan horse and activate drug release upon targeting our newly discovered pathway.\n\n### 💡**Key facts about our solution:**\n\n➔ High killing efficacy\n\n➔ Designed to specifically target pathogenic cells while preserving the surrounding healthy cells and ensuring no toxicity or adverse side effects\n\n➔ Highly suitable to become a platform solution (i.e., therapeutic development pipeline expanded to multiple disease areas and organisms)\n\n➔ Scalable, stable, and easy to manufacture\n\n### \n\n# 👋**Our Asks:**\n\n* Share this post with your network and help spread the word!\n* If you’re as excited as we are about this novel approach, email us to learn more about it at [emmanouela@acxtherapeutics.com](mailto:emmanouela@acxtherapeutics.com)\n\n### ",
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"body": "### **ABOUT VELORUM**\n\n[**Velorum Therapeutics**](https://www.velorumtx.com/) **is developing a new class of cancer drugs that starve tumors by hijacking cancer metabolism. We have shown our drugs work in mice and plan to begin human clinical trials next year, starting with the #1 cancer killer – lung cancer.**\n\n### **OUR SCIENCE**\n\n**We’ve discovered that cancers are addicted to a molecule called ‘heme’.** Heme, which is well-known as the central component of ‘hemoglobin’, is used by the mitochondria to make energy. Cancers ‘steal’ free-floating heme from the blood to fuel their rapid growth. **Our patented drugs starve cancer by removing this heme that cancer cells need, but normal cells don’t.** We’ve shown in animals that our drugs are highly effective for multiple cancers, without affecting healthy cells, even at the highest doses. Also, by starving tumors, we make them weaker and more susceptible to existing cancer treatments, like radiation, chemo, and immunotherapies.\n\nThe body has a natural system to clean up this free-floating heme, but it gets overwhelmed by cancer. **Our drugs essentially amplify the body’s natural heme removal system.** We let nature do most of the work – our drugs are built from proteins that have evolved over millennia to be perfect at capturing heme. Importantly, heme is the exact same molecule across all mammals, meaning the results we see in animals have a higher chance of translating to humans.\n\nWe’re starting with lung cancer, with a lead drug advancing to first-in-human trials next year. **Because multiple types of cancers have this same vulnerability, our lead drug could potentially be used for lots of cancer types and become a standard-of-care treatment.**\n\n### **OUR TEAM**\n\n[Parsa](https://linkedin.com/in/parsa-modareszadeh) and [Pouya](https://linkedin.com/in/pouya-modareszadeh) are twin brothers and scientists who spent their entire adult lives studying how cancers make energy. They worked together on this breakthrough science over the past 5 years, helping develop this promising new class of cancer drugs. They founded Velorum to advance this breakthrough to patients, surrounding themselves with a team of world-class drug developers, scientists, and clinicians.\n\n\n\n### **CONTACT US**\n\nTo learn more about our science or support us on our mission, please reach out at [info@velorumtx.com](mailto:info@velorumtx.com)",
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"body": "**About us**\n\nHello 👋 We’re [Evita](https://www.linkedin.com/in/evita-weagel/) and [Rachel](https://www.linkedin.com/in/rachel-brog-garlick/), co-founders of [Eris Biotech](https://www.linkedin.com/company/eris-biotech). We met working at the same lab at BYU when Evita was a Ph.D. student and Rachel was an undergrad. We collaborated on a peer-reviewed paper and conference presentations, and we’ve been friends since! Rachel went on to Dartmouth for her Ph.D. and later worked at a startup developing cell therapies. Evita went on for a postdoc and a career in drug development in big pharma. Rachel has developed multiple novel therapies for patients and Evita has brought numerous drugs to the clinic. We’re excited to bring our expertise together to build a first-in-class treatment that follows our mission to aggressively fights cancer, but also improve the patient experience.\n\n\n\n**Our Backstory**\n\nEvita, working in drug development, saw promising cancer drugs fail because they were simply too toxic. Rachel, in cell therapy, saw the same problem as treatments that worked in principle caused too much collateral damage. Both realized that the real issue was not just the drugs themselves but the way most cancer treatments rely on toxic approaches that also wipe out our best weapon against cancer, the immune system. Tumor immune evasion is one of cancer’s greatest strengths, yet few were tackling it head-on. We founded Eris Biotech to change that and to develop therapies that stop tumors from hiding and reawaken the immune system.\n\n**The Problem**\n\nOne of cancer’s greatest weapons is its ability to hide from the immune system. Tumors release billions of **tiny vesicles** that contain “don’t attack me signals” and shut down the immune system, blinding it to their presence. This immune evasion lets cancer spread unchecked while most current treatments, such as chemotherapy, radiation, and even some immunotherapies, further weaken the body’s natural defenses.\n\n**Our Solution**\n\nEris Biotech develops small-molecule drugs that prevent tumors from forming and releasing immune-suppressing **tiny vesicles**. By stopping this process, we remove cancer’s disguise, restore immune surveillance, and give the body’s natural defenses the upper hand. Essentially, our drug doesn’t kill cancer directly like toxic chemotherapies – instead, our drug awakens the immune system to fight cancer for you. This approach targets one of cancer’s most powerful survival strategies while avoiding the high toxicity of traditional treatments. We’re starting by targeting mesothelioma, a cancer of the lung lining, with a 5-year survival rate under 13 percent.\n\n**Contact us**\n\nIf you want to learn more about our science and mission, you can reach us at [**founders@erisbio.com**](mailto:founders@erisbio.com)",
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"body": "---\n\n**TLDR:** **We build fast and experimentally accurate generative AI-based simulations of molecular interactions for pharma and biotech companies.** These simulations can tell us whether a drug is going to bind to a protein or how quickly a drug will act once it is consumed by a patient.\n\n---\n\n# Our Team\n\n\n\nWe are a team from the University of Cambridge with 30+ years of combined experience in AI and Molecular Modelling, including:\n\n* [Javier](https://scholar.google.com/citations?user=_b-Cs2cAAAAJ), who scaled probabilistic AI methods 1000x during his PhD, turned down a research scientist position in Big Tech to start Angstrom AI.\n* [Laurence](https://scholar.google.com/citations?user=vMd1gwMAAAAJ), who developed FAB, a method for training AIs from physical equations without training data, during his PhD and was a researcher at InstaDeep before its acquisition by BioNTech in 2023.\n* [Miguel](https://scholar.google.com/citations?user=BEBccCQAAAAJ), Javier and Laurence's PhD supervisor, author of foundational research on AI for drug discovery and generative modeling.\n* [Gabor](https://scholar.google.com/citations?user=q39javYAAAAJ), who developed MACE, a state of the art quantum mechanically accurate force field.\n\nMiguel and Gabor's work has been cited over 40k times.\n\n# The Problem\n\nDuring drug discovery and development, pharmaceutical companies need to understand how drug molecules interact with other molecules in the human body to determine the drugs’ efficacy and safety.\n\nConventional methods to assess molecular interactions are unsatisfactory:\n\n* Wet lab experiments are accurate but slow and expensive.\n* Machine learning prediction methods, like AlphaFold, are fast but inaccurate. They are limited by the quality and quantity of training data, which must be generated by lab experiments.\n* Molecular Dynamics simulates interactions by rolling out the equations of physics, offering a balance between wet lab and machine learning predictions in accuracy and speed. These factors depend on the model of physics used. More accurate models are more expensive to run. This makes the method compute bottlenecked rather than data bottlenecked.\n\nHere is a video of a simulation of 64 water molecules. \n\n# Solution: Fast and Accurate Molecular Simulations\n\nWe run molecular simulations, keeping us in the compute constrained regime, but we combine **1) quantum mechanically accurate models of physics 2) generative AI that allows us to run these models quickly.** \n\n1. We use the MACE (multi-atomic cluster expansion) physics model, which accurately reproduces quantum-mechanical interactions. It was developed by Gabor, our co-founder. 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"body": "**Tl;dr**: Finding new cures requires knowing **what** and **where** the abnormal molecules are in a diseased tissue, but current tools see less than 1% of those molecules. [Biocartesian](https://www.biocartesian.com/) combines microscopy and new chemistries to see a comprehensive picture, offering unprecedented insights into disease biology and new therapeutic approaches.\n\n\n\n# **👩🔬 Team**\n\nWe’re [**Ian**](https://www.linkedin.com/in/ian-dardani) and [**Jess**](https://www.linkedin.com/in/jessxli/), two former UPenn PhDs on a mission to see every molecule in biological specimens. We’ve spent years advancing and troubleshooting the latest tools in biology, from DNA sequencing to microscopy, but even with these efforts, these tools revealed only a fraction of the underlying biology. We dreamed of a way for scientists to see everything (and fast) so they could reduce their guesswork and focus on the most promising therapeutic approaches.\n\n# ❌ **Problem**\n\nMost diseases remain a mystery because existing methods can see less than 1% of the molecules within a biological specimen. These methods can also take months for scientists to set up and often fail, wasting thousands of dollars per specimen and millions in failed drug programs.\n\n# 🔬 **Our approach**\n\nWe’re combining new chemistries and microscopy to see a full map of the molecules in biological specimens. Imagine if we could see into the \\~500 million existing tumor specimens to decode these cancers.\n\nTo use our technology, scientists simply ship their biological specimens to our laboratory, where we automate their conversion into digital molecular maps – saving them months of tedious data collection and troubleshooting.\n\n\n\nOur solution:\n\n* Enables scientists in any disease area to 50X their data collection\n* Saves scientists months of time\n* Lowers the cost of biological measurements\n* Expands access to bespoke assays developed by any scientist by integrating with our platform\n* Meets the growing demand for biological data as AI-led analysis supersedes the bottleneck of human-led analysis\n* Turns biology from 99% invisible to an interpretable problem\n\n# **🙏 Asks**\n\nIf you’re excited about this and would like to learn more, email us at:\n\n[**interested@biocartesian.com**](mailto:interested@biocartesian.com)\n\nor reach us at ",
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"title": "Anto Biosciences: A Foundation Model for Microbial Communities",
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"body": "We’re [Arvid](https://www.linkedin.com/in/arvid-gollwitzer/) and [David](https://www.linkedin.com/in/daviddegruijl/), and we’re building [Anto Biosciences](https://www.linkedin.com/company/anto-biosciences).\n\n### tl;dr\n\nAnto is building a multimodal foundation model for microbial communities, making the gut microbiome computable for the first time. We predict drug toxicity and efficacy across diverse populations and optimize molecules for universal efficacy — addressing the microbiome-driven causes of drug response and failures.\n\n**Launch Video**: https://youtu.be/BDPp2wXiDbw\n\n### The Key Problem\n\n**We caught a cancer drug that worked in China but failed in U.S. trials. Same molecule, different microbiomes. $1B lost.**\n\nThis isn't an isolated story; more than a billion people are currently on medications where the microbiome dictates success or failure. The gut microbiome drives drug toxicity and efficacy, but pharma lacks the tools to understand and act on it \\[10,11,13\\]. Current methods to simulate the gut take months, cost millions, and rely on animal models that are too different from humans to be useful.\n\n### About Anto\n\n**We predict drug toxicity and efficacy across diverse populations and optimize molecules for broader efficacy—addressing the hidden, microbiome-driven causes of drug response and failures.**\n\nInput any drug molecule. We tell you how people will respond. We show where it works and where it fails—by geography, age, or diet.\n\nWe predict:\n\n* Population-specific efficacy (geographic, age-based, vegans vs. meat eaters, etc.)\n* Failure mechanisms (specific microbial metabolic pathways) \\[9,11\\]\n* Treatment response profiles across diverse populations \\[10,14\\]\n\nAnd we don't just flag risk; we identify failure mechanisms down to microbial metabolic pathways and generate treatment response profiles across populations. Then we optimize the molecule for broader efficacy. Turning 20% efficacy into 80% becomes an optimization problem we can actually solve.\n\n**Why we can do this:** We've pioneered quality-aware, goal-directed sparsification algorithms \\[5,9,15\\]. Microbiome data is 99% noise. We remove most of the noise and low-quality data through sparsification, leaving only the 1% that actually carries predictive power. That's how we make the microbiome computationally tractable. \\[6,7,8,14\\].\n\n### About Us\n\n[Arvid](https://www.linkedin.com/in/arvid-gollwitzer/) — Broad Institute of MIT and Harvard, ETH Zurich. Nature-published researcher who pioneered microbiome sparsification. Built the methods that make noisy microbial data tractable.\n\n[David](https://www.linkedin.com/in/daviddegruijl/) — ex-ETH Zurich, J&J, BWH, Harvard Med, Gastroenterology. Struggled with current drug digestion models firsthand. Knows where current workflows break and how to fix them.\n\n\n\nWe’re second-time founders who have lived this problem and published at leading AI conferences and Journals.\n\n### Traction\n\nWe're working with pharma R&D to simulate drug–microbiome interactions and de-risk cross-border deals with population-specific efficacy predictions and response profiles. Our open-source tools and research \\[1-9,14,15\\] are widely adopted and taught in engineering and computational biology programs \\[16\\].\n\n### What This Means for Digital / Computational Biology\n\n**The microbiome is the missing layer in drug development.** Most orally administered drugs are modified by gut bacteria before reaching their targets. We're making this layer computable for the first time \\[11-14\\].\n\nIf you're building in digital biology, precision medicine, or drug development, let's talk. The microbiome affects everything.\n\n### Our Asks\n\nIf you’re in drug development, cross-border deals, or translational medicine, we’d love to learn what microbiome requirements matter most to you. If you know teams in pharma and believe this product is cool, sharing / intros would mean a lot.\n\nIf you are in academia or microbiome research, try out our open-source tools \\[5-15\\]!\n\n**Get in touch**: [founders@anto.bio](mailto:founders@anto.bio) | [anto.bio](https://www.anto.bio/)\n\n### Our Research\n\nOur foundational work spans high-performance genomic analysis \\[1-4\\], metagenomic classification algorithms \\[5,9,14,15\\], in-storage computing for microbiome data \\[6-8\\], and the first large-scale datasets for causal modeling of microbial ecosystems \\[11,12\\]. We've pioneered techniques that make microbial communities computationally tractable, including quality-aware sparsification that reduces noise by 99% while preserving predictive power \\[5,9,15\\]. Our recent work on therapeutic resistance \\[10\\] and AI-controlled metagenomics for clinical applications \\[14\\] directly informs our drug optimization capabilities.\n\n**References:**\n\n\\[1\\] N. M. Ghiasi et al., \"GenStore: A high-performance in-storage processing system for genome sequence analysis,\" in _Proc. 27th ACM Int. Conf. Architectural Support for Programming Languages and Operating Systems_, 2022, pp. 635-654.\n\n\\[2\\] N. M. Ghiasi et al., \"GenStore: In-storage filtering of genomic data for high-performance and energy-efficient genome analysis,\" in _2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)_, 2022, pp. 283-287.\n\n\\[3\\] N. M. Ghiasi et al., \"GenStore: A high-performance and energy-efficient in-storage computing system for genome sequence analysis,\" _arXiv preprint arXiv:2202.10400_, 2022.\n\n\\[4\\] M.-D. Rumpf, M. Alser, A. E. Gollwitzer et al., \"SequenceLab: A comprehensive benchmark of computational methods for comparing genomic sequences,\" _arXiv preprint arXiv:2310.16908_, 2023.\n\n\\[5\\] A. E. Gollwitzer et al., \"MetaTrinity: Enabling fast metagenomic classification via seed counting and edit distance approximation,\" _arXiv preprint arXiv:2311.02029_, 2023.\n\n\\[6\\] N. M. Ghiasi et al., \"MetaStore: High-performance metagenomic analysis via in-storage computing,\" _arXiv preprint arXiv:2311.12527_, 2023.\n\n\\[7\\] N. M. Ghiasi et al., \"MetaStore: High-performance metagenomic analysis via in-storage computing,\" _arXiv e-prints_, pp. arXiv-2311, 2023.\n\n\\[8\\] N. M. Ghiasi et al., \"MegIS: High-performance, energy-efficient, and low-cost metagenomic analysis with in-storage processing,\" in _2024 ACM/IEEE 51st Annual Int. Symp. Computer Architecture (ISCA)_, 2024, pp. 660-677.\n\n\\[9\\] A. Gollwitzer et al., \"MetaFast: Enabling fast metagenomic classification via seed counting and edit distance approximation,\" _arXiv preprint arXiv:2311.02029_, 2023.\n\n\\[10\\] A. E. Gollwitzer, D. A. Subramanian, I. Tucker, and G. Traverso, \"Steering the evolutionary game: Hierarchical control of therapeutic resistance in cancer treatment,\" in _NeurIPS 2025 AI for Science Workshop_, 2025.\n\n\\[11\\] A. E. Gollwitzer, D. A. Subramanian, I. Tucker, and G. Traverso, \"MetaOmics-10T: The foundational dataset to unlock causal modeling of microbial ecosystems,\" in _NeurIPS 2025 AI for Science Workshop_, 2025.\n\n\\[12\\] N. M. Ghiasi et al., \"MegIS: High-performance, energy-efficient, and low-cost metagenomic analysis with in-storage processing,\" _arXiv preprint arXiv:2406.19113_, 2024.\n\n\\[13\\] H. J. Haiser and D. de Gruijl, \"Emerging tools and technologies for microbiome-aware drug development,\" _Clinical Pharmacology & Therapeutics_, 2025, doi:10.1002/cpt.70026.\n\n\\[14\\] A. Gollwitzer et al., \"AI-controlled metagenomics: High-throughput taxonomic profiling for clinical pathogen detection and early-stage cancer screening and type classification,\" _arXiv preprint arXiv:2311.02029_, 2025.\n\n\\[15\\] A. Gollwitzer et al., \"The Thinking Microscope: A reinforcement learning framework for the co-optimization of computational and generative data sparsification in metagenomics,\" _arXiv preprint arXiv:2311.02029_, 2025.\n\n\\[16\\] A. E. Gollwitzer, \"Decoding the human microbiome through AI-controlled metagenomics: Promises and ethical implications,\" presented at ETH Zurich ETHix Series, Dec. 2024. \\[Online\\]. Available [here](https://bioethics.ethz.ch/news-and-events/news-bioethics/2024/12/decoding-the-human-microbiome-through-ai-controlled-metagenomics-promises-and-ethical-implications.html).",
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"votes": 32,
"created_at": "2025-11-07T20:30:08.883Z"
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"full_name": "Shaamil Karim",
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{
"id": 107031,
"title": "Atlas Discovery: AI-native pharma company",
"tagline": "Autonomous research agents and models for drug development",
"body": "TL;DR: We’ve built the foundations of a pharma in the age of superintelligence: data partnerships, biology models and autonomous research agents. We have SOTA results on finding overlooked drugs and predicting clinical trial success rates. \n\nHey everyone! We ([Sanjukta Bhattacharya](https://www.linkedin.com/in/sanjukta-b-82b541198/), [Christian Gensbigler](https://www.linkedin.com/in/christian-g-a2594029a/), and I) started Atlas Discovery to get treatments to patients who need them most.\n\n**The problem:** Today's pharma works for only \\~150 diseases. Below roughly $150M in expected revenue a program never gets funded, because the cost of developing a drug is fixed by the knowledge work it requires, not the size of the market. 10,000+ diseases remain without an approved treatment even where the biology is tractable. Meanwhile, there are 100,000+ existing drugs, and barely 1% of the drug-disease combinations have ever been tried. The bottleneck is no longer supply of compounds; it's the judgment needed to search that space and run the trials, and the removal of human coordination costs.\n\n**Our** **thesis** is that AI will drop the cost of drug development enough to push the viable threshold from \\~$150M toward \\~$15M, opening a \\~$500B market incumbents have structurally ignored. A company built for that world needs three things: proprietary data, models that predict clinical trial outcomes, and agent systems that replace knowledge work. We use agents to repurpose existing drugs for the long tail of diseases, then run the clinical trials faster and cheaper. \n\n**Progress**: In three months we've built the first version of all three layers. Data: an 8 figure LOI with an AI lab and 6 figures in contracted revenue. Models: state-of-the-art clinical trial prediction results published at three conferences,. Agents: three rare disease foundations are now testing drugs surfaced by our agents. \n\nOn drug repurposing using agents, we see nearly 5x improvement over current frontier LLMs. Read more about this benchmark [here](https://atlasdiscovery.bio/repurposingbench.html).\n\n\n\n\nWe've also shown that models trained on in vitro cell data (Latin for \"in glass\") and mouse data can translate to gains in predicting patient response to drugs in human clinical trials.\n\n\n\nWe’ve also shown SOTA results in being able to predict clinical trial success when trained on over 100,000 clinical trials data and evaluated on a held out set of 10,000 trials. Read more [here](https://atlasdiscovery.bio/clinical-trial-prediction-with-ai-agents.html)\n\n\n\nWe’ve presented our research in 3 top conferences (ICLR, CSHL, ICML) and shown that new AI architectures like discrete diffusion can learn these gene expression patterns and predict response to treatments with over 10x improvement in accuracy while training on 50x less data.\n\n\n\nReach us at [founders@atlasdiscovery.bio](mailto:founders@atlasdiscovery.bio)\n\nBlog: blog",
"url": "https://www.ycombinator.com/launches/RqJ-atlas-discovery-ai-native-pharma-company",
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