Rebecca Neeser
@rebeccaneeser.bsky.social
300 followers 180 following 9 posts
PhD student @EPFL in Schwaller and Correia labs | previously @ETH Zurich and @MIT
Posts Media Videos Starter Packs
rebeccaneeser.bsky.social
7/n
Also thanks to my wonderful coauthors @igashov.bsky.social , @rne.bsky.social , @mmbronstein.bsky.social and supervisors @pschwllr.bsky.social and Bruno Correia. ❤️
rebeccaneeser.bsky.social
6/n
I’ll be presenting this work at:
🧬 GEMbio workshop: Sun 27th (Hall 4#4)

🔬 AI4Mat workshop: Mon 28th (Topaz Concourse).

If you're around #ICLR2025, let’s chat! 😊
rebeccaneeser.bsky.social
5/n
We also propose a robust evaluation framework:

✅ “Hard” fragment recovery

✅ “Soft” pharmacophoric similarity

This gives a nuanced view of what the model learns – and shows improvements over docking-based screening baselines.
rebeccaneeser.bsky.social
4/n
This means:

🔹 You can flexibly explore new fragment libraries

🔹 No retraining required

🔹 Outputs stay valid & structure-aware

🔹 More expressive than vanilla virtual screening
All in one unified latent space ✨
rebeccaneeser.bsky.social
3/n
We then extended this to a generative flow matching framework:

🧠 It learns distributions over fragment latents & spatial arrangements

🧪 Conditioned directly on protein surfaces

✅ No decoder needed

✅ Chemically realistic by construction
rebeccaneeser.bsky.social
2/n
💡 Fragment encoder:

We first train a protein–fragment encoder with contrastive loss to map both fragments and protein surfaces into a shared latent space.
It captures interaction-relevant features, which can be used directly for fast virtual screening 🚀.
rebeccaneeser.bsky.social
1/n
Fragment-based design = build better drugs by combining small fragment that each have key interactions.

But:

❗Fragments bind weakly

❗Standard screening is inefficient

So we built a contrastive learning model to learn how fragments interact with protein pockets. 🧬
Reposted by Rebecca Neeser
rne.bsky.social
Our paper on computational design of chemically induced protein interactions is out in @natureportfolio.bsky.social. Big thanks to all co-authors, especially Anthony Marchand, Stephen Buckley and Bruno Correia!

t.co/vtYlhi8aQm
Reposted by Rebecca Neeser
pschwllr.bsky.social
We are hiring (resharing appreciated)!

Given recent successful grant applications (I got my SNSF Starting Grant 🚀), we are extending the LIAC team with multiple openings (PhD/postdoc) for 2025.

Apply now (deadline: December 20th) by filling in this form: forms.fillout.com/t/eq5ADAw3kkus.
#ChemSky
rebeccaneeser.bsky.social
We openend the call for talks for the „AI & the Molecular Woles“ track at @appliedmldays.bsky.social 2025. See you there!
rne.bsky.social
Excited about the intersection of AI and molecular sciences? Join us at the 'AI & the Molecular World' track @appliedmldays.bsky.social in Lausanne on Feb 13!

The call for talks is now open: t.co/zisz2g3HLU
(please submit your proposals before January 5th)