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Sphinx Bio

Sphinx Bio

Software Development

Empowering scientists to make better decisions, faster

About us

Biotech is pushing the frontier of bio in amazing ways: designing proteins from scratch, predicting protein structure, extracting insights from scientific literature, and much more. To take advantage of these recent advances, scientists need better software than the current tangled mess of spreadsheets, notebooks, and slide decks. Sphinx's mission is to empower scientists to make better decisions, faster. We're building a data platform that allows biotech companies to focus on their science and ML, not their data infrastructure. If you’re excited to build software for cutting-edge science that saves lives, please reach out. https://www.sphinxbio.com

Industry
Software Development
Company size
2-10 employees
Headquarters
San Francisco
Type
Privately Held

Locations

Employees at Sphinx Bio

Updates

  • Sphinx Bio reposted this

    View profile for Nicholas Larus-Stone

    CEO @ Sphinx Bio | Better software for scientists

    Speed is the most underrated thing in biotech. A common response is "But cells take time to grow" or "biological/clinical time scales are just longer" (or less charitably "what does this software person know about the wet lab anyways"). I'm not denying that there are certain physical limitations that are hard to change. But that doesn't mean you can't move faster in every aspect — even when doing novel science. Let's take an example near and dear to my heart: Sometimes when I talk to scientists, they seem skeptical that saving a few hours on their data processing and analysis is worthwhile. But let's examine what happens if you need to wait on your computational team to process the data. Say you're running a reasonably complex 3 day experiment and you get a readout at 4pm on Wednesday. If you need to wait for someone else to process the data and get back to you, you might not get the results until Thursday morning. Now it's too late to set up the next experiment until next Monday. And maybe that keeps happening until your timelines for in vitro validation have slipped enough that you missed the window to get into in vivo studies and have to wait until next month... A couple of hour ballooning into weeks or months is an extreme example, but this happens on smaller scales every day! Speed *is* important and should be prioritized everywhere, even in early R&D processes. After all, we're all trying to do the same thing: get life saving medicines to patients as quickly as possible.

  • Sphinx Bio reposted this

    View profile for Nicholas Larus-Stone

    CEO @ Sphinx Bio | Better software for scientists

    Most data is only looked at once. “But we review everyone’s results again at lab meeting.” That’s still only looking at the data in the context of what you know now. Most companies never go back and revisit old data *with the additional insights garnered from new data*. Part of the reason this doesn’t happen is that it’s time consuming and difficult to do so. Part of it is that you’re not sure if you’ll uncover anything valuable if you do so. But a nontrivial number of new experiments could be avoided if we looked at old data. We can catch systematic technical or biological errors earlier. Shaving time off of experimental timelines is valuable… and it’s easier to do than you think. You might have heard people talking about “lab in the loop” or “lab of the future” and thought this was something that didn't exist yet or your company isn't ready for. However, it’s not as complicated as it seems. The capabilities to continuously monitor your data and ask questions of your entire experimental history already exist. You just need to take advantage of them. So the next time something goes wrong in the lab, ask yourself: could I have avoided this issue by looking at all my data?

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  • Sphinx Bio reposted this

    View profile for Nicholas Larus-Stone

    CEO @ Sphinx Bio | Better software for scientists

    I know there's a lot of interest in AI within biotech/pharma companies — yet also skepticism around outlandish claims. It can be hard to see how AI is going to help accelerate your timelines. Your science is unique and your data is incredibly valuable, but you don't have time to waste on hype that isn't going to speed up getting your drug into patients. At Sphinx Bio, our platform has already helped leading biotechs reduce their experimental cycle times through practical applications of AI. But we know that you might not be ready for a full-fledged platform. That's why we're launching Sphinx Services. We partner with biotechs and pharma to build specific solutions that accelerate R&D timelines and reduce project risk: https://lnkd.in/gqdxwC_2. Our team of engineers and AI experts will help you: - Transform historical experimental data into predictive insights - Automate manual workflows to free up your scientists - Derisk key decision points with data-driven recommendations Ready to discuss how we can help you get to the clinic faster? Let's schedule a quick discovery call.

    Services

    Services

    sphinxbio.com

  • Sphinx Bio reposted this

    View profile for Nicholas Larus-Stone

    CEO @ Sphinx Bio | Better software for scientists

    I'm super excited to show a sneak preview of what we've been working on at Sphinx Bio -- automated parsing of ELN entries + instrument files. Have you ever recorded unstructured data in an ELN entry? Has your teammate? Do your pipelines break when experimental designs change? If the answer to any of those questions is yes, you probably know how painful it is to analyze experimental data in an R&D setting. That's why we've been working on an AI agent that can automatically extract the relevant information from your ELN entries and instrument files, transform the data, then join them together to create a single, analysis ready dataset. Take a look at how it works below and come find me at #SLAS2025 if you're interested in learning more!

    Luciferase Assay · Benchling - 24 January 2025

    Luciferase Assay · Benchling - 24 January 2025

    https://www.loom.com

  • Sphinx Bio reposted this

    View profile for Nicholas Larus-Stone

    CEO @ Sphinx Bio | Better software for scientists

    "We need an AI strategy for our lab data!" "Have you automated your data processing and standardized how you analyze your assays yet?" "Well... no." This conversation happens more often than you'd think. While everyone's chasing the next big thing, the real opportunity is making routine lab work more reliable and efficient. The reality is: - Many scientists spend 15-20% of their time on basic data processing and routine analysis - Manual analysis introduces errors and inconsistencies - Delays between running experiments and analyzing results slow down discovery The good news? These problems are solvable today. The key is starting with clear templates and workflows for data processing and analysis. When labs get these basics right: - Scientists save hours per week - Results are more reliable and reproducible - Teams can actually develop an AI strategy Don't put the cart before the horse -- get your data strategy in place before you start slapping AI on things.

  • Sphinx Bio reposted this

    View profile for Nicholas Larus-Stone

    CEO @ Sphinx Bio | Better software for scientists

    I'm expecting to hear a lot about AI at JPM this week, but I suspect there will be too much focus on AI for molecular design and not enough on AI for broader biotech efficiency. Look, AI is the hot buzzword right now and everyone wants to make sure they're attractive to investors/potential partners. However, you should make sure you're buying into the appropriate amount of hype at the right time. While AI driven molecular design will be an important part of the future of biotech, AI enabled workflows are an important part of unlocking your current team TODAY. If your team is building data pipelines, searching for data or previous experiments without neural search, manually building one-off analyses, using Google Scholar for papers, writing INDs by hand, or doing ANY sort of manual, time-consuming labor on a computer -- you're failing to take advantage of the true current benefits of AI. Imagine if every scientist and engineer on your team was suddenly 25% more productive. That's what the top performing teams and companies are seeing today. So, have that conversation about using AI to discover new targets or design a best-in-class molecule. But don't forget about to talk about how to leverage AI everywhere else in the drug discovery process as well.

  • Sphinx Bio reposted this

    View profile for Nicholas Larus-Stone

    CEO @ Sphinx Bio | Better software for scientists

    "Can you rerun this analysis in Python?" If you're a bench scientist, these words probably make your heart sink. If you're in computational biology, you've probably said them more times than you can count. And if the end of that sentence is R instead of Python, then everyone is unhappy... This disconnect between bench and computational teams isn't just frustrating - it's costing biotechs precious time in their race to develop new drugs. The truth is, most routine biochemical assay analysis follows standard patterns. It shouldn't require either manual spreadsheet work or custom coding. The future of biotech isn't about making everyone a programmer - it's about creating systems that let scientists focus on science. What's the biggest challenge you've faced in collaborating across the bench-computational divide?

  • Sphinx Bio reposted this

    View profile for Nicholas Larus-Stone

    CEO @ Sphinx Bio | Better software for scientists

    Your assay data is telling you something important. But can you hear it over the noise of: - Spreadsheets scattered across drives - Results buried in slide decks - Analysis redone multiple times by different teams - Hours spent just getting data into the right format At most biotechs, scientists spend more time searching for and managing data than actually analyzing it. The result? Delayed decisions, frustrated teams, and valuable insights left undiscovered. While everyone's science is different, the fact of the matter is that 80% of routine assay analysis follows the same basic patterns. By standardizing data ingestion and analysis, scientists can focus on hard questions, not data wrangling. The biotechs moving fastest aren't just doing more experiments - they're extracting more value from each one. What's holding your team back from faster insights? Comment below or tag someone who's passionate about modernizing drug development workflows.

  • Sphinx Bio reposted this

    View profile for Nicholas Larus-Stone

    CEO @ Sphinx Bio | Better software for scientists

    Excited to announce we're hiring for a Senior Software Engineer! We've seen a lot of interest in what we've been building over the past few months and we'd like to bring on an experienced engineer to help scale our systems. If you're interested in helping improve human health and fix the climate, like LLMs, and want to work on hard problems -- please reach out! It's an exciting time for us at Sphinx Bio and we're looking forward to bringing you on to helps us to build the next generation of biotech infrastructure. See more details here: https://lnkd.in/gzsfZznV

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