Narbe Mardirossian

at Terray Therapeutics

[Terray Therapeutics @Terray_Tx]

Terray has been built at the intersection from the moment it was founded, and we recently expanded our digital and visual footprint to reflect this.

Our CEO, Jacob Berlin, PhD and CFO, Eli Berlin went into the philosophy at Terray, what makes us stand apart, and how we are bringing together experimentation and AI to transform the speed, cost, and, most importantly, success rate of small molecule drug discovery.

Check out our new video about our approach and how EMMI, our platform for drug discovery and development, enables our teams to innovate.

For the full blog check out the link in comments!

[johnparkhill @j0hnparkhill]

Congrats to Matteo Rossi, Ryan Pederson, Stephan Thaler, and the rest of the team on the ICML paper. https://t.co/wycOvL4lPE

[Terray Therapeutics @Terray_Tx]

Last week we announced a big leap forward for universal potency models with EMMI Predict: TerraBind. TerraBind delivers best-in-class potency prediction while running 26x faster and reducing inference cost by 96% relative to standard industry potency models.

A key driver of this performance is our collaboration with @nvidia and the integration of the NVIDIA cuEquivariance library. By centering its computational core on triangle attention and multiplication, TerraBind's architecture is uniquely positioned to fully leverage cuEquivariance's GPU-optimized kernels, providing a 3-4x speedup compared to standard implementations.

With EMMI Predict: TerraBind at the center of our Generate-Predict-Select workflow, this combination of proprietary data, purpose-built models, and NVIDIA-accelerated hardware is transforming how we discover and optimize small-molecule therapeutics. We're excited to keep pushing the boundaries of predictive chemistry with partners like NVIDIA as we work to bring better medicines to patients faster.

Read our blog https://t.co/ShSfob1PX4
Read our arxiv paper https://t.co/VrQurS3gPV

[Andrew Dunn @AndrewE_Dunn]

NEW: Terray has taken a contrarian AI approach, building a non-diffusion-based model for binding affinity predictions

They shared some H2H results against Boltz-2, a popular open-source model, showing 20% more accuracy + 26x faster to run:
https://t.co/BFZU8y6qI8

[Ben Pouladian @benitoz]

Most AI drug discovery tools can only evaluate thousands of molecules at a time.

Powered by Nvidia, Terray's new model TerraBind can screen billions 26x faster and with better accuracy.

Faster screening = faster cures 🚀🧬 Proud investor in @Terray_Tx

https://t.co/zAxZcDzzqU

[Terray Therapeutics @Terray_Tx]

Today we announced EMMI Predict: TerraBind, our universal potency model that dramatically expands Terray’s ability to move molecules to the clinic faster. Far outpacing publicly available models, TerraBind is a cornerstone of Terray’s end-to-end workflow for small molecule discovery and development.

Read our blog: https://t.co/ShSfob1PX4

[Anthony Costa @anthonycosta]

Awesome new results and great use of NVIDIA accelerated computing libraries for life sciences!

[Narbe Mardirossian @nm_dft]

Incredible website: https://t.co/xDUUll1K5t

[Narbe Mardirossian @nm_dft]

@kooshiar Did you try Gemini?

[the tiny corp @__tinygrad__]

How does this have traction? What gains does anyone think this gets over GPUs to get 20x over H100s, or do they assume the audience isn't sophisticated enough to ask that question with any rigor and just sees nonsense like "burned the transformer into the chip"?

[Rep. Joe Neguse @RepJoeNeguse]

BREAKING — Senator Lee has just withdrawn his public lands sell-off provision!

Thanks to your efforts, the American people — and our public lands — have won!

🏔️🏔️🏔️

[johnparkhill @j0hnparkhill]

Definitely a cool dataset! An Allegro-Tensormol Behaving physically and optimizing off RdKit after like 30k rows of training. After a little validation, happy to OS for anyone who like minimalist, scalable, charged UFFs.

[johnparkhill @j0hnparkhill]

We're Hiring. https://t.co/bkHa5wQWdU

[Narbe Mardirossian @nm_dft]

@CorinWagen @olexandr @AriWagen @RowanSci Add the closest local minimum from each docked pose to your conformer ensemble. Quick fix for now.

[Narbe Mardirossian @nm_dft]

@owl_posting 😂 maybe they meant to write: "Validated (through binding assays) hundreds of binders that are either > 10uM or <= 5 atoms/residues different from a clinical or FDA-approved compound"; there it's fixed 😬

[Patrick Walters @wpwalters]

I'm thrilled to announce a new preprint describing collaborative work with @prof_ajay_jain and Ann Cleves Jain, "Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows".

https://t.co/8WzcGYlQAQ

[Narbe Mardirossian @nm_dft]

@AriWagen @CorinWagen @bernadsz @OrbMaterials @OpenCatalyst From a quick glance, the GFN2-xTB results don't look right. What's being shown on the main page? MAEs in kcal/mol? WTMAD-2? How can the error for GFN2-xTB be 9.54 for Inter-NCIs but none of the individual datasets are anywhere close to that value?

[johnparkhill @j0hnparkhill]

Happy several team members got to be in Times Square for this.

[Endpoints News @endpts]

❗️EXCLUSIVE❗️
Terray Therapeutics has raised a $120 million Series B round, aiming to get its first drug candidate into the clinic while advancing in its AI-fueled approach to making molecules.

https://t.co/mLyyqHMoW3