Benchling

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[Benchling @benchling]

What does it mean to be on #TeamScience? Find out what Benchlings have to say about our mission and culture — and above all, why we’re so passionate about helping scientists power new possibilities for humanity. https://t.co/vbtHiX04Z4

[Nicholas Larus-Stone @nlarusstone]

Day 5 of “An agent a day keeps Phase 2 failures away” — literature review

Literature Review is probably the most common way I see scientists use AI. AI tools have come a long way since the early days of ChatGPT when they used to hallucinate references, but most scientists are still not using AI to its full potential. If you set up a lit review agent that is *based on your actual experimental data* you will get much better results.

Sample prompt (feel free to tweak): Create a weekly Literature Review agent that runs every Friday morning and surveys the latest literature relevant to my work. In particular, look for papers that include new techniques that could accelerate my science or papers that contain data that might confirm or refute my current research direction.

[Feng Zhang @zhangf]

Thank you to Ed Pilkington and John Levy for making biology so exciting, and to the many teachers, students and colleagues I've learned from since. Grateful to @stephgreed and @NatRevChem for this birthday Q&A.

https://t.co/WcH8VWxaOb

[Nicholas Larus-Stone @nlarusstone]

This is actually the path to having useful AI agents in GxP environments as well

[Nicholas Larus-Stone @nlarusstone]

Day 4 of “An agent a day keeps Phase 2 failures away” — the little things

Sometimes it’s the little things that add up and cause delays. Like not knowing if a reagent is in stock or whether your sequencing core has finished with your samples. The last thing you want is to have to run around figuring something out in the middle of your experiment. So why not spend a few minutes up front planning to avoid hours of disruption later. That’s what our Daily Experiment Planner agent can help with.

Sample prompt (feel free to tweak): Create an Experiment Planner Agent which runs every weekday morning. It should look at what I did yesterday and my calendar today to determine what my schedule for today should be. Please prepare any notebook entries that I need to do my work today and call out any things I should be aware of (reagents running low, equipment I need to reserve, etc).

[Nicholas Larus-Stone @nlarusstone]

Day 3 of “An agent a day keeps Phase 2 failures away” — data quality

Controls are only controls if they behave consistently between experiments. But are you really doing the work of comparing this experiment’s controls against all the previous times you ran the experiment? Or are you just eyeballing that they look ok…

Set up a Control Chart Agent to build and alert based on your control charts. Catch contamination or a bad batch of reagents before you’ve spent months going down the wrong path.

Sample prompt (feel free to tweak): Create a Control Chart Agent which looks at my experiments from the week and creates or updates a control chart from the previous 5 experiments of the same type. Please alert me if anything falls outside of the UCL or LCL and investigate potential causes.

[Benchling @benchling]

New from Benchling and @NVIDIAAI:
Scientists and AI agents will be able to access RFD4-Proteina for generative protein design through Benchling Model Hub.
https://t.co/MPOTfvstjf

[Benchling @benchling]

Agentic lab-in-the-loop will only happen when dry lab computation and wet lab experimentation come together.

See what we’re building with @NVIDIAAI. https://t.co/MPOTfvstjf

[Benchling @benchling]

We teamed up with @NVIDIAAI to post-train Nemotron 3.5 Super VL on scientific protocol data.

It outperformed comparable closed models in troubleshooting and optimizing lab experiments, at a remarkably low cost, representing a promising path for R&D teams to build specialized intelligence they can customize and improve over time with their own data. https://t.co/c1muF3i3mv

[Nicholas Larus-Stone @nlarusstone]

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.

[Nicholas Larus-Stone @nlarusstone]

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.

[Sajith Wickramasekara @sajithw]

30 days of agents that any scientist can instantly setup

[Nicholas Larus-Stone @nlarusstone]

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

[Benchling @benchling]

With Science Foundries, teams of agents handle routine work and surface insights, while scientists review, approve, and make the final calls.

See how it works in this Antibody Discovery Foundry walkthrough with Benchling’s @nlarusstone.
https://t.co/MtP7ghy4yO

[Benchling @benchling]

Benchling Chemistry is officially GA.

Capture structured reaction data, calculate stoichiometry automatically, and run ADMET predictions to your heart’s content. https://t.co/kxAUOECcYu

[Benchling @benchling]

Introducing Direct Data Share.

Keep your scientific data in sync between Benchling and @Snowflake, without building custom data pipelines.

Now available in beta: https://t.co/xWyk8CH3Gc

[Sajith Wickramasekara @sajithw]

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.

https://t.co/bDZJna9Nz6

[BiopharmaTrend @BiopharmaTrend]

Breaking: @benchling just launched Science Foundries, moving from helping scientists with individual tasks to managing entire scientific processes with teams of AI agents.
https://t.co/VUf5NIDDfy

[Nicholas Larus-Stone @nlarusstone]

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: https://t.co/hNkykCHQwe

[Benchling @benchling]

Just announced at Benchtalk: Science Foundries.

Foundries bring together specialized agents and models to run entire scientific processes, whether it’s protein production, assay development, or lead optimization.

A team of agents handles tasks and coordinates handoffs, while scientists still make the important decisions.

More from Benchling’s @nlarusstone: https://t.co/YWGKkiJ8b0

[Benchling @benchling]

Love a good Gantt chart? Use Benchling AI to vibe code your own lab project tracker.