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Rebecca Neeser

@rebeccaneeser.bsky.social

318 followers 181 following 9 posts

PhD student @EPFL in Schwaller and Correia labs | previously @ETH Zurich and @MIT

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Rebecca Neeser @rebeccaneeser.bsky.social · 24/04/2025

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. ❤️

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Rebecca Neeser @rebeccaneeser.bsky.social · 24/04/2025

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! 😊

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Rebecca Neeser @rebeccaneeser.bsky.social · 24/04/2025

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.

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Rebecca Neeser @rebeccaneeser.bsky.social · 24/04/2025

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 ✨

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Rebecca Neeser @rebeccaneeser.bsky.social · 24/04/2025

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

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Rebecca Neeser @rebeccaneeser.bsky.social · 24/04/2025

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 🚀.

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Rebecca Neeser @rebeccaneeser.bsky.social · 24/04/2025

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. 🧬

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Rebecca Neeser @rebeccaneeser.bsky.social · 24/04/2025

Hello from Singapore 🇸🇬! Thrilled to be at #ICLR2025 presenting our work on fragment-based drug discovery 🧩. We go beyond virtual screening with a generative, structure-aware approach. 📃 openreview.net/forum?id=bZW...  🔗 github.com/rneeser/Late... A thread 🧵👇

openreview.net

Flow-Based Fragment Identification via Contrastive Learning of...

Fragment-based drug design is a promising strategy leveraging the binding of individual fragments, potentially yielding ligands with multiple key interactions, surpassing the efficiency of full...

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Reposted by Rebecca Neeser

Arne Schneuing @rne.bsky.social · 15/01/2025

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

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Reposted by Rebecca Neeser

Philippe Schwaller @pschwllr.bsky.social · 02/12/2024

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

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Rebecca Neeser @rebeccaneeser.bsky.social · 21/11/2024

We openend the call for talks for the „AI & the Molecular Woles“ track at @appliedmldays.bsky.social 2025. See you there!

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