Deniz Kavi

(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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Reliant AI Emerges from Stealth with $11.3M for GenAI Biopharma Data Analysis - HIT Consultant Reliant AI Secures $11.3M Funding for GenAI Biopharma Data Analysis #ReliantAI #Funding Reliant AI Unveils GenAI Platform for Biopharma Data Analysis #GenAI #Biopharma GenAI Platform Utilizes AI for Drug Discovery and Development #AI #DrugDiscovery Reliant AI Aims to Revolutionize Biopharma Industry with Advanced Data Analysis #BiopharmaIndustry #DataAnalysis Investors Show Confidence in Reliant AI's Potential for Innovation in Healthcare IT #HealthcareIT #Innovation Reliant AI Poised to Disrupt Traditional Biopharma Data Analysis Methods #Disruption #DataAnalysis Overall, Reliant AI's GenAI Platform Promises Exciting ai.mediformatica.com #analytics #launch #provider #data #analysis #biopharma #generativeai #dataanalysis #lifesciences #aipowered #biopharmaceutical #brain #digitalhealth #healthit #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/46RcwsN)

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🚀 Your One-Stop Shop for Accelerating Drug Discovery with BioLens - AI Powered Biomarker Discovery Platform 🚀 BioLens offers AI as a Service in a streamlined, one-stop shop model, tailored to small and mid-sized drug discovery companies. Our AI-powered platform is designed to accelerate the drug development process, while our collaboration with disease experts ensures precise, actionable insights. Through our cost-effective, shared risk and reward model, you can leverage cutting-edge technology and expert knowledge without the burden of high upfront costs. BioLens aligns with your success by tying costs to performance-based outcomes, allowing for scalable solutions that grow with your needs. 🔬 Why Choose BioLens? AI as a Service: Access BioLens’ advanced AI tools and analytics, providing customized solutions across biomarker discovery, clinical trial matching, and patient stratification, all without the complexity of developing in-house AI capabilities. Disease Expertise: Benefit from our network of disease experts who bring deep, domain-specific knowledge to ensure your drug discovery process is grounded in real-world clinical insights. One-Stop Shop Model: From early-stage research to clinical trials, BioLens integrates data, AI, and expert consultation in one seamless platform, providing all the resources you need to bring therapies to market faster. Shared Risk and Reward: Our performance-based pricing means you pay only for measurable outcomes, aligning our interests and ensuring mutual success at every stage of your development journey. With BioLens, you gain access to the most advanced AI tools and disease-specific expertise to accelerate discovery, mitigate risks, and bring life-saving therapies to market more efficiently. 💡 Ready to explore how BioLens can be your one-stop shop for drug discovery and AI-driven insights? Let’s discuss a tailored partnership that drives innovation and growth. #OneStopShop #AIasAService #DrugDiscovery #AIinHealthcare #DiseaseExpertise #BiomarkerDiscovery #ClinicalTrials #PharmaPartnerships #LifeSciences #PrecisionMedicine #SharedRiskReward #BioLens

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Ginkgo Bioworks now offers programmatic access to its LLM model for protein design! In collaboration with Google Cloud, Ginkgo luanched application programming interface (API) access a machine learning model called AA-0, which has been trained on more than 2 billion protein sequences from Ginkgo’s proprietary data. Ginkgo’s API includes capabilities such as Masked Language Modeling and Embedding Calculation: ✔ Masked Language Modeling: Given a sequence of amino acids with missing segments indicated by mask tokens, the model predicts and fills in the gaps, allowing for precise protein sequence generation. ✔ Embedding Calculation: This function computes valuable representations of protein sequences by extracting data from the model's final hidden layer. These embeddings can be used for downstream tasks, such as clustering and classification, enhancing the ability to derive insights from protein data. Additionally, Ginkgo’s approach includes access to both proprietary and publicly available models, like ESM2, allowing users to explore various methodologies within a single platform. The API introductory pricing is set at approximately $0.18 per million tokens, allowing users to perform predictions on around 2,000 protein sequences for 20 cents. Check it out! Disclaimer: I own a super-tiny long position in Ginkgo and planning to increase it. Not a financial advice, investing in biotech stocks is super risky. #biotech #LLM #proteindesign #biopharmatrend Image credit: Ginkgo

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👀 Sales reps when Prospify Beta drops: ℹ️ (We’re building the 1st AI Agent to scale GTM sales to unexplored leads. Prospify analyzes companies’ sales pipelines, and scours the open web, surfacing 10 hyper-targeted prospects daily). 🔗 Become our champion, and get early access: https://lnkd.in/dCjWK4XA #AI #Agents #GTMAI #ML #Salesautomation

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🚀 Planning for JPM Week? Get actionable insights from Tyler Mizenko on scaling biotech labs efficiently in our latest blog post. Don’t miss these strategies before the big event: https://hubs.li/Q030pt5K0 #JPM2025 #Biotech #ScalingSuccess

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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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🚀 Kick off Conf42: Platform Engineering with an exciting opening talk by Amit Taneja that will reshape how we think about technology in the pharma industry. 🌐https://lnkd.in/eJrMnJGS Discover how cutting-edge AI-driven platforms are revolutionizing drug discovery and engineering the future of healthcare. 📅 Don’t miss the first session on **September 5, 2024**—it’s a must-attend for anyone passionate about tech and innovation! #Conf42 #PlatformEngineering #PharmaTech #AIinPharma #TechInnovation #DrugDiscovery #HealthTech #AIPlatforms #EngineeringFuture #DigitalHealth #Biotech #PharmaInnovation #TechInPharma #AIRevolution #HealthcareInnovation #LifeSciences #MachineLearning #BiomedicalEngineering #TechConference #FutureOfMedicine

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ClinChoice Extends 13-Year Medidata Partnership with Addition of Clinical Data Studio to Improve Data Management, Strengthen Clinical Trial Capabilities #Storyline: https://lnkd.in/gUbMJ9vi via DailyCADCAM.com #ClinChoice #ClinicalDataStudio Medidata Solutions #ClinicalTrials #LifeSciencesIndustry Dassault Systèmes Sachin Nalawade DailyCADCAM Deepa Nalawade DailyCADCAM

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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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Revolutionizing Drug Discovery: Aqemia Raises $38M to Mesh Theoretical Physics with Generative AI The biotech sector is witnessing groundbreaking innovation, and Aqemia is at the forefront. This Paris-based pharmatech company has raised $38M to accelerate drug discovery by combining quantum-inspired physics and generative AI (GenAI). Unlike traditional AI systems, Aqemia generates its own data using advanced physics algorithms, enabling faster and more precise identification of drug candidates. Founded in 2019, Aqemia has already partnered with major pharmaceutical companies and is scaling projects to revolutionize healthcare. The company's mission? To make drug discovery as efficient and scalable as a tech project. With this funding, Aqemia is poised to reshape how we approach critical diseases, fostering innovation at the intersection of AI and science. #Biotech #AI #Innovation #DrugDiscovery #TechLeadershipAQEMIA

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Reimagining Healthcare Workforce Platfforms: Turning Capital Into Labor The future isn’t just about filling shifts. It’s about scaling labor intelligently, driving better outcomes for systems, staff, and patients alike. Andreessen Horowitz and Alastair (Alex) Rampell reinforced this Vision in a recent post and it applies to the next phase of healthcare workforce evolution. Healthcare workforce marketplaces plus AI are at the tipping point of transformation, evolving into systems of record that don’t just manage labor—they optimize it. Much like EHRs centralized patient care data, these marketplaces can centralize workforce operations, aligning talent supply with clinical demand. But there’s a bigger opportunity here: turning capital into labor. Here’s what I mean. Historically, industries (including healthcare) have focused on enterprise computing tasks—streamlining data entry, compliance, and back-office functions. While valuable, this approach often stops short of transforming the real-world, repetitive processes that make up the bulk of work in healthcare. Think beyond enterprise software. The next frontier lies in leveraging embodied AI and workforce marketplaces to automate and enhance labor-driven processes, transforming capital investments into productive labor outputs. For healthcare, this means turning staffing costs into scalable, dynamic systems that deliver value far beyond filling shifts: 1. Dynamic Labor Matching: Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2. Workflow Automation: From credentialing to compliance and shift bidding, embodied AI can streamline traditionally manual tasks, freeing human resources for higher-value work. 3. Labor as a Strategic Asset: Workforce marketplaces shift the focus from static staffing budgets to proactive labor deployment, ensuring that every dollar invested in labor drives outcomes and efficiency. It is about the billions of repetitive, planned tasks that AI can improve. In healthcare, that means automating everything from staffing logistics to clinical task assignments, creating a horizontal system of engagement for labor (think Zendesk) that integrates seamlessly with vertical, workflow-specific platforms (think Toast for staffing operations). The result? Hundreds of billions in untapped market potential—and a healthcare workforce model that transforms how care is delivered. By turning capital into labor through smarter marketplaces and automation, we don’t just solve staffing challenges; we redefine what’s possible in healthcare operations and ShiftMed is leading the way with health systems moving beyond tired models. #capitalintolabor #systemsofrecord #healthworkforce #aiworkforce #healthsystems #hospitals #cno #hospitalceo #healthcareinnovation

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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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𝗧𝗵𝗲 𝗛𝗼𝘄𝗮𝗿𝗱 𝗛𝘂𝗴𝗵𝗲𝘀 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗜𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗲 (HHMI) has announced AI@HHMI, a $𝟱𝟬𝟬 𝗺𝗶𝗹𝗹𝗶𝗼𝗻, 10-year investment to integrate AI into biomedical research. AI@HHMI will implement an "AI-in-the-Loop" approach, placing AI at the core of the scientific process. This strategy will utilize AI systems to design experiments, build automated pipelines, collect AI-ready data, and create learning models to infer underlying biological principles. ➜ Check the news at: https://lnkd.in/djwF8vSq ➜ Vendorvise's official Instagram: https://lnkd.in/ebK2KQ2C ➜ Vendorvise's Linktree: https://lnkd.in/dQdbMdth 🤖 If you enjoy reading my posts about AI and tech news, hit the like button and share with your friends to support my content! #ai #artificialintelligence #ainews #aipowered #news #blog #hhmi #500million #biomedical #research #scientific #data #generativeai #aitech #technology #content #socialmedia #future #new #business #techtrends #innovation #launch #success #markets #people #industry

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LLMs in Life Science have Roadblocks to Discovery – Part 1: Emerging Science LLMs are great at sequencing DNA because it is a big and narrow data problem – patterns of repeating base pairs. But then come additional layers of bioinformatics and analytics to try to understand the genotype-phenotype connections. This is a diverse and sparse data problem, which is the Achilles heel of LLMs. Understanding the human genome, however, is not understanding human life. Recently we discovered that the human microbiome, the full array of microorganisms (the microbiota) that live on and in humans and, more specifically, the collection of microbial genomes that contribute to the broader genetic portrait, or METAGENOME, of a human. The genomes that constitute the human microbiome represent a remarkably diverse array of microorganisms that includes bacteria, archaea (primitive single-celled organisms), fungi, and even some protozoans and nonliving viruses. Bacteria are by far the most numerous members of the human microbiome: the bacterial population alone is estimated at between 75 trillion and 200 trillion individual organisms, while the entire human body consists of about 50 trillion to 100 trillion somatic (body) cells. The sheer microbial abundance suggests that the human body is in fact a “SUPRAORGANISM,” a collection of human and microbial cells and genes and thus a blend of human and microbial traits. https://lnkd.in/erqj5Qzs But it doesn't stop there. Growing research into the PHAGENOME is exposing additional complexity to biomedical discovery, focusing on what some researchers labeled biological “dark matter,” as yet unidentified viruses and organisms of extraordinary variety, potentially expanding the human metagenome. The phageome: A hidden kingdom within your gut | Knowable Magazine Emerging science is continually expanding the scale and scope of diverse and sparse data that is critical to learning but is invisible to LLMs. But there is an even more significant roadblock to learning and discovery for LLMs – the nature of human language. I will discuss that in my next post.

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