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Ahmet Sarigun

@asarigun.bsky.social
29 followers 106 following 8 posts

curious explorer - asarigun.github.io

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Reposted by Ahmet Sarigun
Saez-Rodriguez Group @saezlab.bsky.social · 29/09/2026
Happy to see the peer-reviewed version of SpaCEy now out: 📄 Published paper: www.nature.com/articles/s41... 💻 Code: github.com/saezlab/SpaCEy
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Ahmet Sureyya Rifaioglu @ahmetrifaioglu.bsky.social · 17/12/2025
🎉 Happy to share our new work SpaCEy, now available🧬 SpaCEy is an explainable method for learning clinically relevant spatial tissue organization directly from spatial omics data—without predefined cell types or regions. 📄 Paper: www.biorxiv.org/content/10.6... 💻 Code repo: github.com/saezlab/SpaCEy
biorxiv.org
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Matthieu da Costa @mdc-biocat.bsky.social · 31/08/2025
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)
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Ahmet Sarigun @asarigun.bsky.social · 15/09/2025
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!
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Reposted by Ahmet Sarigun
Max Delbrück Center @mdc-berlin.bsky.social · 12/09/2025
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.”
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Gina El Nesr @ginaelnesr.bsky.social · 13/07/2025
is this how small molecules bind?? 😼
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Radoslav Krivak @rdkbio.bsky.social · 27/06/2025
🚀 GPU-accelerated docking to P2Rank-predicted pockets
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Ahmet Sarigun @asarigun.bsky.social · 26/06/2025
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!
arxiv.org
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...
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Ahmet Sarigun @asarigun.bsky.social · 26/06/2025
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)
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Ahmet Sarigun @asarigun.bsky.social · 26/06/2025
• Achieves state-of-the-art success rates on physically valid pose prediction • Works across ligand flexibility levels and diverse, unseen protein targets (4/n)
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Ahmet Sarigun @asarigun.bsky.social · 26/06/2025
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)
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Ahmet Sarigun @asarigun.bsky.social · 26/06/2025
...physically realistic ligand poses — and are not always as efficient or accurate as often claimed. (2/n)
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Ahmet Sarigun @asarigun.bsky.social · 26/06/2025
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)
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Open Free Energy @openfree.energy · 04/02/2025
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.
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Ahmet Sarigun @asarigun.bsky.social · 03/02/2025
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! :)
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Leif S. Ludwig @leifludwig.bsky.social · 31/01/2025
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!
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Pia Rautenstrauch @prauten.bsky.social · 24/01/2025
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...
biorxiv.org
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...
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