Molecular Machine Learning
@molecularML
Team-run account of the Molecular ML research group led by @fra_grisoni | #AI for Drug Discovery | Doing all this at @tueindhoven & @ICMStue
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Happy two-year anniversary to the @molecularML team! 🎂 What a journey it has been so far — filled with immense satisfaction, cool collaborations, and incredibly talented people. A toast to stretching the limits of #AI in drug discovery and chemical biology 🥂
How can AI help us discover new molecules, materials, and medicines? Join us for ML4Molecules 2025, part of the ELLIS UnConference in Copenhagen 🇩🇰 on Dec 2 co-located with #EurIPS 📣 Call for contributions open until Oct 15 (extended)! 🔗 More info: eurips.cc/ellis
#DreamReactions2025: Congratulations to award winners! 🍃Green Dream Reactions Award🏆 to @fra_grisoni and poster awards to researchers from Mülheim, Münster, Würzburg and Amsterdam. Special thanks to Reinhard Hoffmann (center)!
Scaffold Hopping with Generative Reinforcement Learning pubs.acs.org/doi/10.1021/ac… @foo_fighterin @fra_grisoni #JCIM Vol65 Issue13 #MachineLearning #DeepLearning
Molecular task arithmetic reduces off‑target drift: It keeps other physicochemical properties closer to the pretrained baseline than standard finetuning That subtlety matters for downstream ADMET
Read the paper: Look the Other Way: Designing 'Positive' Molecules with Negative Data via Task Arithmetic arxiv.org/abs/2507.17876
💥 Negative data ≠ useless noise for molecules ⚙️ Train on 10k negatives → walk another way → zero‑shot, dual‑objective +24k hit clusters with higher diversity than classic finetuning Negatives-only Tradeoff: ‑14 % success‑rate & -16% validity → blend a few actives to…
New paper alert 👇
Our perspective on deep learning for de novo design is now published @JCIM_JCTC! We curated hundreds of works for beginners and experts, covering: 🔮 generative models 📝 synthesis planning 📊 benchmarks 🧪 translation to wet-lab 🎯 challenges ahead
Our perspective on deep learning for de novo design is now published @JCIM_JCTC! We curated hundreds of works for beginners and experts, covering: 🔮 generative models 📝 synthesis planning 📊 benchmarks 🧪 translation to wet-lab 🎯 challenges ahead
Our work on predicting protein binding sites with deep learning is now online! The work has multiple outputs: Preprint: chemrxiv.org/engage/chemrxi… Web app of the model: 14-3-3-bindsite.streamlit.app Python library for peptide processing: doi.org/10.1093/bioadv… Let’s break it down! 🧵👇
New paper alert 👇
Our DeepCocrystal is now online @angew_chem! DeepCocrystal is a deep learning model that predicts co-crystallization of small molecules from molecular strings. Convolutions shine again✨ AND, its predictions are tested in the lab! 🥼🧪
Our DeepCocrystal is now online @angew_chem! DeepCocrystal is a deep learning model that predicts co-crystallization of small molecules from molecular strings. Convolutions shine again✨ AND, its predictions are tested in the lab! 🥼🧪
Great to see our work out in @angew_chem! 🎉 We introduce ‘supramolecular’ language processing to predict co-crystallization — with experimental validation! 🙈 Led by the unstoppable Rebecca Birolo, w/ @Rza_ozcelik; @TUeindhoven, @molecularML; @unito 💪🏻 onlinelibrary.wiley.com/doi/10.1002/an…
Great to see our work out in @angew_chem! 🎉 We introduce ‘supramolecular’ language processing to predict co-crystallization — with experimental validation! 🙈 Led by the unstoppable Rebecca Birolo, w/ @Rza_ozcelik; @TUeindhoven, @molecularML; @unito 💪🏻 onlinelibrary.wiley.com/doi/10.1002/an…
🔈 New preprint alert!
Proud of @hlnbrnkmnn’s first PhD paper on rethinking how SMILES augmentation is performed for generative #DeepLearning! 🚀 Check it out! 👇 📄 chemrxiv.org/engage/chemrxi… 🖥️ github.com/molML/fantasti… @molecularML @TUeindhoven
Proud of @hlnbrnkmnn’s first PhD paper on rethinking how SMILES augmentation is performed for generative #DeepLearning! 🚀 Check it out! 👇 📄 chemrxiv.org/engage/chemrxi… 🖥️ github.com/molML/fantasti… @molecularML @TUeindhoven
Are you using generative #DeepLearning for de novo molecule design?🧪 🖥️ Then check out @Rza_ozcelik ‘s latest work, where we perform a (super) large scale analysis (~1 B designs!) & find ‘traps’, ‘treasures’ and ‘ways out’ in the jungle of generative drug discovery. 🌴 🐒 👇
📢 "How to evaluate de novo designs?" 🤔 If you were ever intrigued by this question, we have a fresh preprint for you! We dig into evaluating design libraries and discover pitfalls and solutions — all in a jungle analogy! arxiv.org/abs/2501.05457 A thread 🧵
"The Jungle of Generative Drug Discovery: Traps, Treasures, and Ways Out" by @Rza_ozcelik , @fra_grisoni Paper: arxiv.org/abs/2501.05457 #machinelearning
📢 "How to evaluate de novo designs?" 🤔 If you were ever intrigued by this question, we have a fresh preprint for you! We dig into evaluating design libraries and discover pitfalls and solutions — all in a jungle analogy! arxiv.org/abs/2501.05457 A thread 🧵
Preprint alert! ‼️
Are you using generative #DeepLearning for de novo molecule design?🧪 🖥️ Then check out @Rza_ozcelik ‘s latest work, where we perform a (super) large scale analysis (~1 B designs!) & find ‘traps’, ‘treasures’ and ‘ways out’ in the jungle of generative drug discovery. 🌴 🐒 👇
If you are doing peptide informatics, check out our 'tidy and tiny' python package! Should be useful especially for new researchers to start building machine learning pipelines. Code, preprint, and documentation are available 🙂
peptidy: A light-weight Python library for peptide representation in machine learning - Researchers have introduced peptidy, a lightweight Python library that facilitates converting peptides into numerical representations suitable for machine learning. This tool could accelerate…
The surprising ineffectiveness of molecular dynamics coordinates for predicting bioactivity with machine learning Checks whether MD-derived 3D information helps for bioactivity and target predictions over just static 3D information. No 3D wins ;) P: chemrxiv.org/engage/chemrxi…
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