OpenBio retweeted
Nothing makes me happier than people saying they love @OpenBioAI (specifically the design)
OpenBio retweeted
Are you interested/building in AIxBio? Are you in Bangalore?
Me, @anaturalist and @vinayprabhu are organising "AIxBio BLR" at IISc this Saturday, April 25th at 11:30 AM. The goal? Build the community that is missing and help each other in any way possible.
We have Founders, PhDs, Faculty, PostDocs attending the event. If you are building something in the space or have a unique expertise, we would love to have you.
Register on the link below.
OpenBio retweeted
UPDATE: LLMs in biology still have some way to go.
A few weeks ago, I had said that @AnthropicAI models are the best at creating publication level figures. But while adding image support to PyMolAI, I realized that is not the case.
Left: A simple pose from a paper I wanted to recreate.
Right: What Sonnet 4.6 was able to make.
They don't look similar at all.
"instead of knowing what a good pose is, the models just know the steps to get to a good pose"
What this means is, that the models don't understand molecular/protein structures. They just have been trained on enough pymol commands to get to a good point. Anthropic models more so than others.
I tested models from @AnthropicAI @OpenAI @Google @Zai_org @MiniMax_AI and @Kimi_Moonshot on whether they can create a publication-level view of a protein-ligand binding site and the results were surprising.
TL;DR: Anthropic models did the best and Gemini models did the worst.
Task was simple:
"Load 5DEL and create a publication ready view of the ORO and FMN binding site"
Here are the results:
1. Sonnet 4.6 from @AnthropicAI:
- Fast
- Used the tools pretty well
- Created the best view with clear labelling and color choices
- always felt in control of what it was going for
- very impressed









