bioengineer | @try_litefold
conserved motif
Joined April 2025
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- Following787
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nabajit (bio/acc) retweeted
As said before, if we are going to do agentic science, lets do it in a right, controlled way, just like how controlled reproducible experiments looks like.
All u gotta do is just connect ur lab.
Ahem ahem. Topic: Own and Customize everything
294 Connectors across target science, mol engineering, developmental biology. DBs, tools, atlases, just ask for it. You can add custom ones.
200+ skills along the same lines. Add custom skills.
Give permissions to all the platform workflows.
Add custom instructions, give permissions what your agents can and can not use and when to ping you. Even create your own subagents.
Compute: Select the compute capacity you need when agentic coding requires gpus.
We have found ways where we have ontologically organized every connector and skill, use jev-like decision models, and cut the token cost by half, and actually getting better accuracy in using the right things in the right time.
As said before, if we are going to do agentic science, lets do it in a right, controlled way, just like how controlled reproducible experiments looks like.
nabajit (bio/acc) retweeted
Glycosylation is among the most important modifications in biology.
We know relatively little about glycobiology, because it is hard to study.
This Nat Rev Methods primer describes our best tools for analyzing glycosylation 🔽
nabajit (bio/acc) retweeted
PhD teaches you time is cheap and money is expensive.
Startup teaches you the inverse.
nabajit (bio/acc) retweeted
Today we are excited to release Artios, a fully open source system for designing functional de novo enzymes.
Enzymes present a new frontier for de novo protein design models due to the challenge of coordinating precise chemistry, rather than binding a target.
Enzymes are life’s biological machines and by designing them we can truly start to engineer biology.
We believe these fundamental capabilities should be built in the open and shared with the community to progress science faster.
🧵 1/6
nabajit (bio/acc) retweeted
China has selected its first group of young researchers to receive funding for up to 15 years, in one of the longest research grants on offer to scientists globally
go.nature.com/4z4Q3pw
nabajit (bio/acc) retweeted
Avengers assemble: 1 year BakerLab + NVIDIA GenAIR = 🔥 rfd4-proteina.org
Presenting RFD4-Proteina, our latest protein design model, significantly better and faster than everything we compared against, both in silico and in the lab! 1/30
nabajit (bio/acc) retweeted
Been testing an internal version of bindcraft on @try_litefold ensembled with RF-3 and so far it's not bad! Btw I can just see the results and loss going down real time (for bc-1)
nabajit (bio/acc) retweeted
A single mutation or PTM can determine whether two proteins bind. But how can we predict binding specificity if our data lose those details? 🥺
Introducing SNOOPPI, a Sequence-Normalized database of On- and Off-target PPIs, now out at #Bioinformatics! 🐶
📜: doi.org/10.1093/bioinformati…
🤗: huggingface.co/datasets/Chat…
nabajit (bio/acc) retweeted
Don't pay ridiculous "gold open access fees" to journals. Release a preprint + publish the paper as "closed access" + upload the final PDF to PMC without bothering about embargo.
If your Author Accepted Manuscript was supported by @NIH funding, it must be posted publicly to PMC without an embargo. There's no cost and it does not require buying expensive open access plans. Take steps to upload your Author Accepted Manuscript here: nihms.nih.gov/login/?next=/s…
nabajit (bio/acc) retweeted
Can we design proteins that cut other proteins? Our de novo cysteine protease paper is out in Nature! We designed new folds that cleave peptide backbones, with activity demonstrated in mammalian cells. Paper and code 👇
nature.com/articles/s41586-0…
nabajit (bio/acc) retweeted
Public preview of BioEmu2! @MSFTResearch AI for Science’s new foundation model for biomolecular structure, dynamics and function. Open weights, MIT license.
github.com/microsoft/bioemu2
Movie: BioEmu2-simulated binding of intrinsically disordered peptide PUMA to MCL-1 receptor. 🧵⬇️
nabajit (bio/acc) retweeted
What does computational protein design look like?
We did a PD-L1 design run using rosalind in LiteFold. We tried to design linear and cyclic peptide candidates to see if we can explore any denovo binders. We used BoltzGen and within a few hours we shortlisted five candidates, with predicted confidence scores ranging from 0.70 to 0.87 and ipTM score from 0.80-0.95
A typical run would usually take days to get good enough binders, but with LiteFold you can do it within hours, including the research like hotspots, pocket scanning etc.,
These are strictly computational binders and not wet lab validated .
Explore protein design with LiteFold.
nabajit (bio/acc) retweeted
if you are feeling hella lost with what’s going on in ML well buddy I have a 1h08min interview with the legendary @vboykis that should set you straight
in it we got:
- good dose of pragmatism
- what you should still learn
- focusing
- how to not lose your braincells to agents
nabajit (bio/acc) retweeted
Agents have to waste soo much token when operating on large files like pdb, single-cell, raw sequencing signal, slide, volume like CryoEM and so many others.
They normally come in binaries when seen in raw formats. Super hard for agent to operate on. Computer use is fine, but then you need 10 different softwares.
What if, you could take the best of both the worlds and merge them?
Soon!
nabajit (bio/acc) retweeted
Well well well, more incoming soon! Open Science ftw!
Hugging Face is becoming more and more the home of AI x Science projects. Open source accelerates scientific discovery when models, datasets, and tools are shared openly, all researchers can reproduce them and build on top of them.
A big thanks to @allen_ai, @OpenMed_AI, and @NVIDIAAI for being the most active contributors of AI for Science artifacts on @huggingface these days!
🤗 Hugging science heatmap: huggingface.co/spaces/huggin…
nabajit (bio/acc) retweeted
Why the fuck are Indians hated so much? What have they done to deserve this level of hatred?
Have Indians promoted terrorism? Have they colonized your countries? Enslaved your people? Stolen your homes? Taken away your families? Kidnapped your children? What exactly have millions of ordinary Indians done to deserve this much racism and hostility?
Is it because they're educated, intelligent, ambitious, hardworking, and competing for the same jobs as you? Since when did intelligence and ambition become crimes? Since when did working hard to build a better life become a reason to be hated?
If your problem is job displacement, then why aren't you directing the same anger toward artificial intelligence?
Why not question AI? Why not question ChatGPT, Claude, Grok, Anthropic, OpenAI?
AI has the potential to displace far more jobs than Indian immigrants ever could. Yet somehow, the Indian engineer, the Indian student, the Indian doctor, and the Indian worker become your enemies.
Why? Because it's easier to target a human being with a different skin colour or accent than to confront the actual economic forces changing your world?
And understand one thing clearly.
If an Indian refuses to engage with your racism, if they ignore your insults, if they choose not to respond to your hatred, it doesn't mean they lack a spine.
It means your hatred isn't worth their time.
Indians don't owe you an apology for being educated, ambitious, hardworking, successful, or simply existing.
Being Indian is not a crime. Being successful is not a crime. And your insecurity is not a justification for racism.
nabajit (bio/acc) retweeted
We’re bringing natural-language control to any scientific generator.
We are very excited to introduce LLM-to-Flow Distillation (LFD), a method to adapt scientific generators to high-level natural-language discovery goals.
Starting from textual goals like “design peptides with promising anticancer activity” or “design small molecules binding to the D2 dopamine receptor”, LFD distills high-level goal-related knowledge from LLMs into scientific generators through iterative DPO on LLM-generated sub-goals.
🔥 LFD does not need task-specific proxy rewards, works with any frontier LLM via API-access, and outperforms state-of-the-art reward-guided methods such as FlowGRPO and DiffusionNFT on held-out computational evaluations. (1/6)
nabajit (bio/acc) retweeted
We’re exploring how AI can accelerate health science research.
While AI has made significant breakthroughs in formal sciences like mathematics, health data science depends on imperfect real-world data and consequential choices about cohorts, variables, and causal assumptions.
To explore how AI can assist health researchers, we built a human-led research scaffold powered by Muse Spark 1.3 and Muse Wonder with access to 5M retrospective de-identified electronic health records and publicly available data. A cohort of medical experts used the system to conduct original research, accelerate hypothesis generation, code execution, and manuscript drafting while maintaining full authority over scientific decisions.
Today, we’re sharing 10 research manuscripts produced through this exploration, spanning cardiology, oncology, infectious disease, emergency medicine, and more.
Read more in our research blog: go.meta.me/3U06wvD