We started @sphinx_bio to empower scientists, and today I'm excited to announce that we'll be continuing that mission as part of @Benchling! There’s never been a better time to build AI tools to help scientists and there's no better place to build those tools than Benchling. Benchling is already used by hundreds of thousands of scientists across the world and we are hard at work building AI agents into the platform to help accelerate research. Keep an eye out for more updates soon!

Day 2 of “An agent a day keeps Phase 2 failures away” -- AI for increasing creativity Apparently yesterday was too tame, so let’s spice it up with a Hypothesis Dreamer Agent. When used appropriately, AI can actually be a great source of creativity. No one (including AI) knows your science better than you do. But sometimes you need to integrate ideas from other people, other teams, or the broader literature. And that’s exactly where an agent which is constantly synthesizing your research with broader context and dreaming up new hypotheses can help. Sample prompt (feel free to tweak): Please create a Weekly Hypothesis Generation Agent that looks over my work, my team’s work, and the published literature to suggest new hypotheses. These can include new research directions, ideas on why a particular experiment isn’t working, or ways our work could be improved. Give a prioritized list of 3-5 actionable suggestions. Please present all of the sources you used to come to this conclusion. If it’s helpful, please use visual references to understand the reasoning behind the suggestions.

30 days of agents that any scientist can instantly setup

Day 1 of “An agent a day keeps Phase 2 failures away” We're going to start with something simple: a Weekly Report Agent. Every scientist I know has to do some sort of communication about what they’re working on. Whether it’s presenting at a lab meeting or discovery meeting, status updates for their boss, or preparing something for a partner. So why not automate the time consuming retrieval of data and creation of plots with an agent? Sample prompt (feel free to tweak): Please create a Weekly Report Agent that summarizes all of the work that I've done over the past week in a slide deck. Make sure to check all of my notebook entries, any sequences or biological entities that I've created, and any other places I was mentioned. Add visualizations where appropriate and link back to the source data.

Launching what I’m calling “an agent a day keeps Phase 2 failures away” It’s come to my attention that many people don’t see how AI can be helpful for biotech. So, every day for the next 30 days, I will be posting an agent that can be helpful for biotech and pharma companies. The agents will span all departments and phases of R&D. Hopefully this will inspire some folks to use AI in novel ways

The objections to this seem to fall into three categories: China, access to capital, and understanding biology. China’s main advantage over the US is their speed. The US will never have as large a workforce or embrace the 996 mentality. Our best bet to compete is to use technology to give individual scientists increased leverage. Biotech’s access to capital has historically gone through many boom and bust cycles. However, the number of approved drugs is not correlated with funding. In addition, AI should be helping save biotechs millions of dollars, reducing the cost to get into the clinic. Finally, I agree AI still isn’t that good at biology. Unfortunately, humans aren’t great at understanding biology either. It doesn’t seem that helpful to say “the biggest problem facing biotech is that most of their drugs fail”. While true, there are more actionable statements. All in all, I definitely have a bit of a hammer problem (everything looks like a nail), but have yet to be convinced by any of our cynical biotech friends away from my position

Every day I’m exposed to horrors beyond manmade comprehension (agent framework wars)

The biggest problem facing most biotechs today is lack of AI literacy

Full house at Benchtalk Boston for @nlarusstone unveiling Science Foundries, a new way to run scientific processes end-to-end with agents and scientists working as a team. benchling.com/blog/introduci…

Multi-agent systems for science are finally here! Just launched Science Foundries with agent swarms, natively multiplayer agents, and more fun stuff We wrote up some thoughts on how this will change scientific discovery as well: benchling.com/blog/introduci…

Now is a good time to mention that @benchling AI is available through Slack! Incredibly powerful to just tag it in a thread and get beautiful plots of your results to resolve a conversation with colleagues

It’s interesting to think through how many other things (Dots, Claude Tag) might be similar to this

“Token spend at 1-2x payroll” Seems similar to reagent spend for scientists, wonder how long it will take biotech/pharma companies to adapt to the fact that tokens are another reagent their scientists need access to

Curious about the future of protein engineering? @bitsinbio has an AMA session with @GabriCorso, CEO of @boltz_bio one of the top protein engineering model companies, starting now until Wednesday! Gabe is on the cutting edge of protein engineering. Boltz has already partnered with 3 of the top 20 big pharma companies: @pfizer, @TakedaPharma and @GSK. Over 100,000 scientists have used Boltz models in just two years. Join the conversation here and get your questions answered by Wednesday: join.slack.com/share/enQtMTI…

Everyone will have a personal agent with their own credentials and then there will be some fixed set of credentials that can interact with personal agents

Sometimes those of us in the business of discovering and developing drugs forget that other people don’t know how the sausage is made

Being a manager prepares you for handling large numbers of agents, unclear if the reverse is true

What would it take to scale scientific intelligence into systems that run a research program end to end, not just assist one step of it? That is the question on Oct 22 in SF. @nlarusstone on the future of the interface between scientists, agents, and experimental data. 
Charbel Eid (BigHat) on closed-loop learning from high-throughput experiments
 @AlfredoAndere on the infrastructure under autonomous research workflows 
Krish Ramadurai (AIX) on what becomes new company categories We’ll also have selected pitches by some of Silicon Valley’s freshest AI for Science startups built by researchers and pharma scientists. Where is AI already doing real scientific work, and where is the gap still widest?

I’m excited to one day explain why AI and bio people keep talking past each other

After polling everyone in the know, it’s been agreed that the models are just not that good at biology (yet)