Aaron Kimball

An AI tool, AlphaFold 3, for drug and vaccine discovery, that simplifies modeling how proteins fold, allowing for easier targeting at the molecular level. You upload the protein sequence to the server and 10 minutes later, you’ve got the shape and the structures. Something that would take years to do otherwise. This is not Gen-AI, but it is as useful or maybe more. AlphaFold3 is limited to non-commercial use through a DeepMind website. The Nature publication's url is in the post.

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A few thoughts from BioIT World's symposium on FAIR data 🤖 It was exciting to see a new focus on making data FAIR for not just humans but also APIs and AI. I hope there will be a new wave of tools to leverage this data--and a positive feedback loop where these tools encourage more data standardization. 🌏 The majority of resources and initiatives are based in Europe. What can America do to incentivize more scientific data transparency? 💰 Is FAIR data economically viable for everyone? When FAIR data is not mandated, scientists may not have the resources (time and money) to make data FAIR. What tools could lower the barrier?

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CRISPR and AI are redefining gene editing. Profluent and Stanford University are leading the charge with their latest breakthrough - AI-designed CRISPR models. No more time-consuming search for organisms. No more slow, resource-draining methods. Welcome to the era of AI-driven CRISPR research. AI models, steeped in protein and genomic sequences, can decode genetic patterns with ease. They create CRISPR designs that surpass traditional tools, offering precise edits and significantly reduced side effects. Meet OpenCRISPR-1. An AI-designed, error-minimizing, gene-editing powerhouse. Better yet, it's open-source. Now everyone can explore this technology, free of charge. The future of precision medicine is here. https://lnkd.in/dxt7vJBg #genomesequencing #biomarkers #aiindrugdiscovery

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#ConvergeSummit panel building on insights. Mary Rozenman discussed 4 key accelerators: - vast availability of data - novel advanced statistical models - greater computing capabilities - tremendous talent in the #biotech industry Abraham Heifets, Tim Luker, Scott Skellenger, and Thomas Miller suggesting that - cultivating data to focus and - building out reinforcement learning are also important in applying #AI in the drug development process.

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Just became an unlikely beneficiary of a deep dive on a strange UnicodeEncodeError on Windows...so that puppy can be automatically tested through cross-platform #Github actions on #Linux and #Windows (yes it's still #Skynet). Next challenge: from zero to a deployed #FastAPI app in one command Take puppy for a walk: https://lnkd.in/gmX-vu9P

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https://lnkd.in/gWUD_7dd Anthropic AI’s Claude family of models represents a great challenging feat for GPT models in AI technology. With the release of the Claude 3 series, Anthropic has expanded its models’ capabilities and performance, catering to various applications from text generation to advanced vision processing. This is an overview of these developments, highlighting the advancements and comparative features of the models within the Claude family.

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Lately, I've been experimenting and building data pipelines for Future Therapeutics and realized how little we talk about data pipelines in cheminformatics. It's a domain where, at any given time, we handle large volumes of data from multiple sources. I'm sharing a special collection of articles on Cheminformatics Data Pipelining, designed to raise awareness and enhance skills in this critical area. Part 1 of 8 is published. Have a look and feel free to share your feedback. Also, let me know if I miss something specific for cheminformatics. I would also love to talk to those actively involved in data pipelining projects to hear your thoughts and approaches. #cheminformatics #drugdiscovery #datapipelines https://lnkd.in/gDa7G2_z

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I like this idea of "technical taste". It gets into where software engineering is as much creative art as it is science. Four great takeaways (IMHO) for software devs in this era in particular: 1. Aspiring engineers should cultivate a sense of curiosity, experiment with different tools and technologies, and embrace a mindset of continuous learning. 2. AI has the potential to streamline processes and enhance productivity for engineers, but it may also lead to disruptions in traditional software development workflows. 3. Developing technical taste and judgment is essential for making informed decisions about which technologies and approaches to pursue. 4. Collaboration and open-mindedness are key to leveraging the full potential of AI and staying ahead of technological advancements. I also really liked these two quotes from Sam Schillace: 1. "The right time to do something is when you have that feeling in the pit of your stomach that's like, 'oh, this is a great idea and it's going to suck to build because nothing's ready yet.'" 2. "Technical taste is like, 'how well have you consolidated that set of experiences and heuristics into judgment that you can apply accurately when you see new things?'" 3. "It may be the case that very small teams can do very large projects, or like we were talking about before, it may be the case we're just going to get really ambitious about what we try to do with the same size of teams, which is kind of where I would put my money." Great job on continuing to put out great episodes, Brett Berson and team First Round Capital! #ai #engineering #softwaredevelopment

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#Pixi+uv is the 2024 way of conda+pip. Best of both worlds for python environments. Refactoring my pet project (https://lnkd.in/gmX-vu9P) with a cross-platform #python-based CLI. Rather than being clever and torture ChatGPT and friends with translating 200-line Bash scripts into #Powershell, went with proper tool for CLI jobs. Click is absolutely delightful to work with. prefix.dev Astral David Lord thank you for your work! P.S. I take it back that Meta.ai is "at least as competent as ChatGPT in coding tasks". Meta is ok at simple things, but for more complex problems quickly and confidently makes shlt up. ChatGPT remains a lot more capable. Gemini still useless.

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Exciting News! Our CEO, Prashant Agrawal, appeared on Good Morning America and The Wall Street Journal to talk about our pioneering research on shifting size curves due to GLP-1 drugs. Learn how we're leading the retail transformation: [link] #retailtrends #GLP1 #weightloss #GoodMorningAmerica #impactanalytics #prashantagrawal https://lnkd.in/gtJjDeK4

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Lift and Shift Your Way to Savings: A Biotech Company's Success Story Moving your applications to the cloud doesn't have to be complex. This biotech company achieved a 30% reduction in IT costs and a 39% decrease in downtime by simply lifting and shifting their applications to AWS. This is a great first step for organizations wanting to experience the benefits of cloud computing. More about this topic on AWS official site. And if you want to find out more about cloud cost optimization visit https://umbrelly.cloud/

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💡 Why do we need so many evaluation tools for LLMs? As engineers, we build 'production-ready' LLM products using these metrics. But what happens next? How do we maintain control and ensure reliability? At Feedback Intelligence, we’ve crafted a cookbook to keep your LLMs reliable and aligned with user expectations. 🍲 📖 Give it a read and let’s chat!

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Lately I've been curious to learn more about how AI is impacting biology and drug development. With all of the hype around Generative AI for text, there seems to be a growing discussion around Causal AI to identify new proteins and predict treatment outcomes. I thought I'd share a few helpful things I've read in the area: This article from Century of Bio offers a great explanation of how causal AI is used for protein discovery. It also gives a really approachable breakdown of how this kind of AI works. https://lnkd.in/g3U3fA4D This article from Nature Medicine goes into detail around the benefits of causal AI for predicting medical outcomes and how it can lead to better individual patient predictions: https://lnkd.in/ggnsvD4z This paper looks at the early success of AI developed drugs in the place where it matters most: clinical trials. It's interesting to see that the early pipeline of drugs designed using AI are in line with historical averages for classically developed drugs (around 40%) https://lnkd.in/g3Y6Jf5S I'll be keeping a close eye on this space! #ML #AI #CausalAI #Biology

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Introducing CASPR! 👻 (The Conduit Assistant for Scientific and Pharmaceutical Research) At Conduit, we've been thinking deeply about how AI can assist scientists in the lab every day to achieve faster and better experimentation. That's why we've developed CASPR, our LLM-powered interface that empowers bench scientists to perform complex workflows in an easy-to-use chat interface. These workflows include: 🚀 Target identification with Conduit AI pipelines. 🔬 Identification of similar diseases using phenotypic + gene profiles. 👩‍🔬 Step-by-step guidance and interactive support for assay design. 🔍 Query a knowledge base of public and proprietary structured data. 🔓 Unlock new insights from biomedical literature. Please watch the demo below and reach out if you would like a chat/longer demo :)

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#ObsessivelyEnthusiasticInvestor #UnreasonablyEnthusiasticInvestor #IrrationallyEnthusiasticInvestor at REFASHIOND Ventures: The Industrial Transformation Fund. We look for the people we believe are doing the best work in Industrial Transformation & Supply Chain Technology; Attempting the impossible. Then we go all-in on them. With obsessive and unreasonable enthusiasm. Join us. #IndustrialTransformation #SupplyChain #Innovation #Technology #Startups #VentureCapital #DemandTheImpossible #UnreasonableEnthusiasm #ObsessiveEnthusiasm #IrrationalEnthusiasm 

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Chatbots spewing hate speech is a problem for engineers and security teams alike. Building safe and reliable AI systems is a problem that runs deep! Check out my latest blog to learn about how we got here and what you can do to deploy AI that you can trust. I’ve even drawn up some illustrations to keep you entertained on the way: 

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Oh man, just finished a prep call with Nathan Wesling from the Descript team, for this tech talk next week. This is gonna be the best session yet! If you are looking for practical advice in how or where to use Temporal Technologies, please join!

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This is a very interesting article on sparse vectors, which can help overcome limitations of vector search related to domain specific keywords. And of course, the code in the example is clear and clean! Sparse vectors have a few values and many zeros. Dense vectors have limited or no zeroes. But really, what we're talking about semantic search on terms where the context is not present or harder to infer. The author presents this as specialized keyword search such as medical terms or other domain specific terminology. "These scores reflect the significance of each term in the context of the document or query, guiding the model to allocate more weight to terms that are likely to be more meaningful for retrieval purposes." Highly recommend reading. Nice work Paul Iusztin. And a nod to Qdrant and their vector search technology that supports sparse vectors. https://lnkd.in/gcBWfTg8

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