Rebecca Neeser

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. ❤️

24.04.2025 07:13 — 👍 4    🔁 1    💬 0    📌 0

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! 😊

24.04.2025 07:13 — 👍 1    🔁 0    💬 1    📌 0

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.

24.04.2025 07:13 — 👍 1    🔁 0    💬 1    📌 0

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 ✨

24.04.2025 07:13 — 👍 1    🔁 0    💬 1    📌 0

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

24.04.2025 07:13 — 👍 1    🔁 0    💬 1    📌 0

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 🚀.

24.04.2025 07:13 — 👍 1    🔁 0    💬 1    📌 0

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. 🧬

24.04.2025 07:13 — 👍 1    🔁 0    💬 1    📌 0

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...

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 🧵👇

24.04.2025 07:13 — 👍 20    🔁 4    💬 2    📌 0

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

15.01.2025 16:37 — 👍 65    🔁 25    💬 1    📌 0

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

02.12.2024 10:33 — 👍 101    🔁 71    💬 6    📌 1

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

21.11.2024 22:03 — 👍 12    🔁 2    💬 0    📌 0