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Ahmet Sarigun

@asarigun.bsky.social

27 followers 110 following 8 posts

curious explorer - https://asarigun.github.io/

asarigun.github.io

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Ahmet Sarigun @asarigun.bsky.social · Jun 26

I'm excited to share our new preprint: PocketVina — a fast, scalable, and accurate multi-pocket molecular docking method.

Docking remains essential in early-stage drug discovery, but recent deep learning–based approaches still face limitations in generating...

Thread - (1/n)

Reposted by Ahmet Sarigun

Matthieu da Costa @mdc-biocat.bsky.social · Aug 31

With the release of Boltz 2, is there already a review comparing the efficiency of different (new) docking tools? (I’ve already tried Pocket Vina, which is actually quite good for high-throughput)

Ahmet Sarigun @asarigun.bsky.social · 23d

A direct comparison with Boltz-2 hasn’t been done yet, but it would be interesting to see one between co-folding and the classical/hybrid docking benchmarks!

Reposted by Ahmet Sarigun

Max Delbrück Center @mdc-berlin.bsky.social · 26d

Nearly 50 new cancer drugs are approved each year – but which one fits which patient?

At the #mdcBerlin, @al2na.bsky.social’s team built Flexynesis, a deep learning toolkit to guide precision cancer care.

Learn more:
👉 www.mdc-berlin.de/news/press/u... 👈

Illustration with overlaid text: The image shows a cancer cell in the bloodstream. The overlaid text appears in white, all-caps sans-serif font inside a dark blue rectangular box at the top left. It reads: “USING DEEP LEARNING FOR PRECISION CANCER THERAPY.” A small credit at the bottom right reads: “© Annie Cavanagh / Wellcome Collection.”

Reposted by Ahmet Sarigun

Gina El Nesr @ginaelnesr.bsky.social · Jul 13

is this how small molecules bind?? 😼

Reposted by Ahmet Sarigun

Radoslav Krivak @rdkbio.bsky.social · Jun 27

🚀 GPU-accelerated docking to P2Rank-predicted pockets

Ahmet Sarigun @asarigun.bsky.social · Jun 26

I'm excited to share our new preprint: PocketVina — a fast, scalable, and accurate multi-pocket molecular docking method.

Docking remains essential in early-stage drug discovery, but recent deep learning–based approaches still face limitations in generating...

Thread - (1/n)

Ahmet Sarigun @asarigun.bsky.social · Jun 26

All results, code (MIT License), and data are open and available:

📄 Paper: arxiv.org/abs/2506.20043
📦 Data: zenodo.org/records/1573...
💻 Code: github.com/BIMSBbioinfo...

Huge thanks to co-authors @al2na.bsky.social, @borauyar.bsky.social, and Vedran Franke!

PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning

Sampling physically valid ligand-binding poses remains a major challenge in molecular docking, particularly for unseen or structurally diverse targets. We introduce PocketVina, a fast and memory-effic...

Ahmet Sarigun @asarigun.bsky.social · Jun 26

We benchmarked PocketVina across four widely used datasets (PDBbind, PoseBusters, Astex, DockGen), and introduce TargetDock-AI — a large-scale benchmark of >500K protein–ligand pairs with activity labels from PubChem.

(5/n)

Ahmet Sarigun @asarigun.bsky.social · Jun 26

• Achieves state-of-the-art success rates on physically valid pose prediction
• Works across ligand flexibility levels and diverse, unseen protein targets

(4/n)

Ahmet Sarigun @asarigun.bsky.social · Jun 26

PocketVina offers a robust alternative:
• Identifies multiple pocket centers using P2Rank
• Performs GPU-accelerated docking with QuickVina 2-GPU 2.1
• Completes docking + binding affinity prediction in under 1.5 seconds, with no model training

(3/n)

Ahmet Sarigun @asarigun.bsky.social · Jun 26

...physically realistic ligand poses — and are not always as efficient or accurate as often claimed. (2/n)

Ahmet Sarigun @asarigun.bsky.social · Jun 26

I'm excited to share our new preprint: PocketVina — a fast, scalable, and accurate multi-pocket molecular docking method.

Docking remains essential in early-stage drug discovery, but recent deep learning–based approaches still face limitations in generating...

Thread - (1/n)

Reposted by Ahmet Sarigun

Open Free Energy @openfree.energy · Feb 4

We're changing the field of #compchem by creating free and open-source software for performing alchemical free energy calculations. Our flagship protocol calculates relative binding free energies of protein-ligand systems. Try it out in your browser: colab.research.google.com/github/OpenF...

Artistic rendering of a biochemical model: a small molecule ligand, shown as a ball-and-stick model colored by element, is bound in a pocket in a protein surface, shown as a space filling model colored off-white.

Ahmet Sarigun @asarigun.bsky.social · Feb 3

I remember when I first started learning ML—Andrew Ng offered a Coursera course that uses Octave and covers neural networks for image classification with MNIST. You might find it helpful! :)

Reposted by Ahmet Sarigun

Leif S. Ludwig @leifludwig.bsky.social · Jan 31

Join us to connect with the vibrant #singlecell community.
📢Register for the #ISCO'25 Conference "Innovations in #SingleCell #OMICS" in Berlin!
🗓️ 12-13 May 2025
🎤 Fantastic Keynote and Invited Speakers
🫵🏿 Many slots for talks: submit your abstract
🔗http://isco-conference.eu
Please spread the word!

Reposted by Ahmet Sarigun

Pia Rautenstrauch @prauten.bsky.social · Jan 24

1/4 🧵 Preprint alert: In "Metrics Matter: Why We Need to Stop Using Silhouette in #SingleCell #Benchmarking," we reveal critical flaws in common #Evaluation metrics for #Integration and propose robust alternatives. @uweohler.bsky.social
www.biorxiv.org/content/10.1...

Metrics Matter: Why We Need to Stop Using Silhouette in Single-Cell Benchmarking

Current-day single-cell studies comprise complex data sets affected by nested batch effects caused by technical and biological factors, relying on advanced integration methods. Silhouette is an establ...

www.biorxiv.org