Joshua Meier

Excited to share what Nithin Parsan and I have been working on at YC! At Reticular (YC F24), we're pioneering the future of explainable AI for biology. Check out the post and our first technical blogpost linked in the comments to learn more about what we're focused on.

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🧩 One thing we've noticed with #VentureIQ technology is how well it can distill and communicate complex technical material 🧩 Whether it's a #deeptech venture, a novel #AI application, or even a patent submission, we can quickly distill the business and strategic implications for investors...and sometimes even the founders/PIs themselves! 🤯 Based on our work so far, the tech can handle a wide array of research areas and technological sectors -- anything from blockchain algorithms to vaccine development. It handles the nuance of the field, the specific terminology, and extracts the key concepts which matter for venture-building. 🤓 To demonstrate, I took a patent I helped write with Eric Laber on reinforcement learning. I ran it through #Ventureiq to build a commercial assessment (key differentiators, target markets). Then for fun I had #NotebookLM make a podcast out of it. I've uploaded the result it here (DM me if you'd like the actual report): https://lnkd.in/eiTejsny This is more than just automation or streamlining operations - it's about unlocking clarity and insight when it was previously unattainable, and the ability to do it over and over again no matter the sector or vertical. Interested in more info? DM me so we can chat.

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🗝️ Keys To Biotech Startup Success 📐 Measure Twice ✂️ Cut Once Plotting the trajectory of a company from seed-stage to an “exit” is the single-most important task that management teams frequently deprioritize (and understandably so… building technologies is way more interesting than drawing up timelines and gantt charts!). Regardless of the nature of the startup business, the universal end goal is to maximize shareholder value and provide early investors with the opportunity to cash out. A simple concept that becomes infinitely more complex when put into practice. Luckily, pharmaceutical discovery and development follows a linear path through the FDA’s regulatory framework, making it relatively simple to chart the growth of the company and the milestones that “move the valuation needle.” But the Devil is in the details, and a comprehensive drug development, clinical trial management, and M&A / IPO strategy is absolutely crucial to maximizing shareholder value. 🪙 Maximizing Shareholder Value As we approach a significant milestone with the completion of our Phase 1 study in June 2025, we are poised for transformative growth. Success in Phase 1 will pave the way for Phase 2 and 3 trials, and open doors to strategic partnership opportunities, M&A, and public listings. Here are the activities that lie on Cytonics Corporation's path of shareholder value creation: ✅ First-In-Human: After 6 years and $10M spent in drug discovery and preclinical development, our novel therapy for osteoarthritis, CYT-108, is finally ready for primetime… a first-in-human Phase 1 clinical trial to assess the safety and efficacy of the drug in patients suffering from mild-to-moderate osteoarthritis. ✅ Strategic Collaborations: Successful Phase 1 trials could attract interest from major industry players. With Johnson & Johnson holding a 12% stake in Cytonics, they are well-positioned as a primary potential acquirer or collaborator for Phase 2 clinical studies. ✅ Future Funding: Positive results from our Phase 1 clinical trials may enable us to pursue a substantial $20M funding round for Phase 2, and serve as a strong predicate for a $100M raise to support the pivotal Phase 2/3 clinical trial. ✅ Phase 2/3: Positive Phase 2 and 3 datasets may offer definitive proof that CYT-108 can restore quality of life and mobility for osteoarthritis patients. Large pharmaceutical companies love to partner or acquire drug assets that have been “de-risked” with Phase 2 data… their risk-reward sweet spot. ✅ New Drug Approval: FDA approval would position CYT-108 as the first and only disease-modifying treatment for osteoarthritis. At this stage, Cytonics would consider an Initial Public Offering (IPO) to provide liquidity for our shareholders and possibly raise funds for future drug development programs. #biotechnology #drugdevelopment #investing #arthritis

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This article is inspired by comments from Tony Czarnik and Stefan Ivanov regarding my previous blog post at https://lnkd.in/gFPhXfKW. To clarify, I am writing from the perspective of an AI believer or practitioner, acknowledging the critical viewpoint that claims "AI has shown little in terms of actual achievements in drug discovery." However, I believe it is not sufficient to simply agree with this stance without further elaboration. My focus here is on higher-level concepts rather than practical examples. Here are three key takeaways: 1. Need for Comprehensive Data Collection The current drug discovery process often lacks systematic documentation and relies heavily on subjective reporting, which hampers the ability to draw robust conclusions. Scientists, particularly chemists and biologists, should prioritize automating the collection of extensive data and metadata to create a rich database for analysis. 2. Integration of Mathematical and Data-Driven Approaches Two primary strategies for improving drug discovery are mathematical modeling and data-driven methods. While traditional scientific practices lean toward mathematical modeling for deeper understanding, advancements in AI highlight the effectiveness of data-driven approaches in addressing existing challenges and providing new insights into complex biological processes. 3. Transformative Role of AI AI technology plays a crucial role in drug discovery by enabling scientists to embrace data-driven methodologies that enhance problem-solving capabilities and deepen understanding of biological systems. Combining both mathematical models and data-driven techniques can lead to a more robust framework for innovation in the pharmaceutical industry.

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Though benchmarks for medical AI are still imperfect, there's no question that AI will continue to march towards diagnostic excellence. These trends will place greater emphasis on physician-scientists – those who advance medical knowledge via closed loop clinic/lab interfaces, which can then feed back into large-scale AI systems that serve entire populations. This was all part of the OG vision for the learning health system. source: https://lnkd.in/eh2RM72s

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A question for the software devs out there - do you think that LLMs are to high level languages as high level languages are to assembler ie just another layer of abstraction? Or is the whole field of software development going to go the way of switchboard operators and there will be just be PMs talking to LLMS? 😱

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Make data immediately and efficiently available so folks can focus on fighting diseases instead of battling outdated systems. It is not okay to hinder biotechs from obtaining their clinical trial data in cumulative CSV or Excel files.  Imagine this: Hours go by, and you're at your desk painstakingly sifting through individual data points. Why? Because someone decided that sending cumulative data was too much to ask. No one should have to: 🔹 Request data files from a vendor contact who may not reply in a timely manner. One client experienced this when their primary contact was out for a week, and they were unaware. 🔹 Have to wait for a weekly or monthly file because that's what the data transfer agreement dictates. Another client could only access their data via PDFs, leaving no room for immediate data exploration or analysis. 🔹 Pay an extra cost for more real-time data updates/transfers. Access to all data should be immediate. #ClinicalTrials #Medicine #RealTimeData #Innovation

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(industry) structure prediction: biotech’s cost per approved drug delivered decreases by an order of magnitude over the next decade, due to model progress As one step towards that, the small but mighty Chai Discovery team has released Chai-1, an open-weights, frontier multimodal molecular structure prediction model, on par with AlphaFold3. Chai-1 was shipped after only 4 months of work — it is not an endpoint but a glimpse of what’s coming. More than just a technical milestone, Chai is a signal of the coming structural shift for pharma. Molecular structure and interaction prediction at scale will reduce the time and cost of drug discovery, shifting risk “left” into silico and away from expensive labs and trials. Lower barrier to entry enables scientists to innovate faster and more successfully. The shift of biotech to model-first is exciting for scientists and for society. I can think of few areas where speed of progress is so important. Congrats Joshua Meier Jack Dent Jacques Boitreaud and team! Technical report, open weights and web server for free experimentation: https://lnkd.in/g7SipS_Y

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ChatGPT moment for Drug Discovery Chai Discovery, an AI biology startup, has announced Chai-1, an AI foundation model for molecular structure prediction. Here is why this is important: 1. Proteins can be encoded as text sequences that represent their composition. If you have this sequence, you can typically synthesize the corresponding protein. 2. However, after synthesizing the protein, its exact 3D shape, or structure, isn't immediately clear. 3. Understanding the protein's structure is essential to determine its function and whether it can bind to other proteins, such as a target. For example, you might want to know if the protein can bind to a virus's spike protein. 4. Chai's model predicts the 3D structure of a protein from its sequence. If the predicted structure seems promising, you can proceed with lab synthesis. If not, you can modify the sequence and try again until the desired structure is achieved. Founded less than six months ago, the company has less than ten employees(former OpenAI/Google/Meta) but has already raised nearly $30 million from Thrive Capital and OpenAI at a reported valuation of $150 million. AI has the potential to help us decode biology, and unlock vast new possibilities. It is clear that the most valuable use case for AI will be speeding up scientific breakthroughs.

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I spoke today with computational bio expert Jefferson Parker, PhD (at NullSet Informatics Solutions) for our State of AI in Biotech report. He shared some interesting career advice for those working in AI: 9 out of 10 leaders in biotech want AI in their scientific development, but do not know WHY. 1 out of 10 DO know why. They probably have the best data, and that is a blessed place to be and develop in your career. Always ask why leaders want AI and much more will be revealed. And for leaders, understanding why you want AI (beyond the hype and promises) will lead to a team culture that prioritizes data and let's the best talent flourish. It's win-win.

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Wow - 2 ex-Meta employees launch EvolutionaryScale landing $142m to advance AI in biology. EvolutionaryScale has introduced ESM3, a cutting-edge language model for biology that predicts protein sequences, structures, and functions. This model can simulate millions of years of evolution, generating new proteins with potential applications in medicine, research, and sustainability. ESM3 represents a significant advancement in AI's ability to design and engineer biological systems. There will be comparisons with AlphaFold3, intrigued where this goes... https://lnkd.in/dhtQDtsA #AIinDrugDiscovery #genomics #proteomics

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By leveraging AI with vast genetic databases, we are not only quickening the pace of identifying potential drug targets but also enhancing the accuracy of clinical diagnostics and predictive modeling. Recently, I had the privilege of discussing the profound impact AI is having on pharmaceutical R&D with Kim Branson, GSK's SVP Global Head of Artificial Intelligence and Machine Learning. Listen on Raising Health: https://lnkd.in/g6QUm2-A

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Making Proteins Glow with AI Thomas Hayes and colleagues at EvolutionaryScale, a startup that recently raised $142 million (https://lnkd.in/eAW4iuZH) from Amazon Web Services (AWS), NVIDIA GPU Ventures and Lux Capital, have created ESM3, an AI model focusing on protein design. Their manuscript "Simulating 500 million years of evolution with a language model" introduces esmGFP, a new fluorescent protein. It's as bright as natural GFPs but with a vastly different sequence from its closest known relative. ESM3 doesn't just create glowing proteins. It can predict protein structures, identify functions, and design proteins with specific attributes. The team, which includes former Meta AI-biology researchers, suggests ESM3 acts like an evolution simulator, exploring protein possibilities nature hasn't yet discovered. The team used computational methods to filter and rank their designs before experimental testing, suggesting a combined approach of AI-driven design and computational screening. Beyond accelerating protein engineering for biotechnology and medicine, this technology could also custom-design innovative proteins for industrial processes. How do you think these technologies might reshape protein engineering and biotechnology in the coming years? Read the original preprint on bioRxiv: https://lnkd.in/eJfTSqh2 Look at the Github repository to explore the smallest and fastest model in the family, ESM3-open: https://lnkd.in/esmKMW2S #ProteinEngineering #AI #Biotechnology #SyntheticBiology #Innovation Image Credit: © EvolutionaryScale

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OMG you would think I would LOVE an announcement that Illumina and NVIDIA were working together but honestly this sounds like this morning's second case of Illumina having to give up a lot to get a little. Deal has two parts: (1) Illumina's "get" is to host a bunch of Nvidia tools for LLMs and data analytics in on the Illumina Connected Analytics platform. Also MONAI for spatial cell imaging workflows though not sure what they have to do with NGS. Anyway, this seems like a nice-to-have, maybe a convenience to customers - but I don't think it opens up any new functionality. Doesn't cost Nvidia anything, and they get to add another logo on their partners slide 🤷‍♂️ (2) Illumina (this is the "give" part) will be "enabling DRAGEN algorithms on NVIDIA GPUs". Not sure why this is good for Illumina, and the PR doesn't explain this benefit at all 🤯 DRAGEN is built on a hardware architecture - FPGA - that has nothing to do with GPU at all, in fact it's kind of the opposite. So Illumina is going to have to make a major investment in porting DRAGEN to Nividia GPU for no stated benefit to Illumina. But the benefit to Nvidia is clear: they get to claim yet another customer for their already vast GPU customer base, and they get to big to displace FPGAs in Illumina instruments. Must be missing something here 🤔🤔🤔 (Don't forget to check out our JMP podcast! https://lnkd.in/gU_qpY3S)

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If you are here at #JPM2025 or tuning in through the webcast and want to learn more - send me a message. We’re excited to share how our platform is transforming the #regulatorylifecyle, infusing AI thoughtfully throughout traditional processes to drive efficiency. #biotechshowcase #AIinHealth #regulatoryoperations #medicalwriting

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Unpopular opinion: Most biotech companies aren't ready for AI/ML, and it's not because of their data scientists. It's because of their bench scientists. Here's why: AI/ML requires large, consistent datasets. But in most biotechs, every scientist analyzes their assays slightly differently. They use different thresholds, different ways of normalizing data, different ways of identifying outliers — even different formulas for basic calculations. This inconsistency makes it impossible to compare results across experiments, let alone build AI models. "But we have SOPs!" you might say. Sure, for running the experiments. But for analyzing the data? Rarely. The hard truth is that without standardized analysis protocols, your data is useless for AI/ML. And implementing those protocols means taking away some of the autonomy bench scientists are used to. It's a tough pill to swallow, but it's necessary if biotechs want to harness the power of AI. At Sphinx Bio, we're building tools that make it easy to implement and enforce these protocols, without sacrificing the flexibility scientists need for exploratory analysis.

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This one struck a nerve, but there was some misinterpretation -- I'm not suggesting dry lab teams blame their wet lab counterparts! Rather, science is noisy and difficult, so let's not add additional sources of variation at the analysis steps. Standardize what you can and your AI/ML initiatives will be much better off. This requires better tooling, yes, but it also requires education and teams to work together. If your tools don't bring together your teams, then you probably have the wrong tools. The most successful companies of the next decade will be ones which blend software and science natively.

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