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Excited to share our new preprint! 🎉🔔 “The surprising ineffectiveness of MD coordinates for predicting bioactivity with ML” Read it: chemrxiv.org/engage/chemrxi… Huge thanks to: Rıza Özçelik Derek van Tilborg Francesca Grisoni Molecular Machine Learning #MachineLearning #ComputationalChemistry

The surprising ineffectiveness of molecular dynamics coordinates for predicting bioactivity with machine learning 1. The study challenges the assumption that molecular dynamics (MD)-derived coordinates are superior for machine learning-based bioactivity predictions, revealing

Finally, officially open! Open #postdoc position in Organic/Medicinal Chemistry for generative #AI in Drug Discovery — join us Molecular Machine Learning TU Eindhoven 💪🏻 Funded by European Research Council (ERC) Deadline: Jan 15, 25 More info below👇 jobs.tue.nl/nl/vacature/po… Repost appreciated! 🔃

Come join us 👇

🎄Christmas is perfect for reading papers, right? RIGHT? Anyways... Here is one we recently published Digital Discovery! pubs.rsc.org/en/content/art…

In our next featured article, Özçelik and Grisoni provide a "hitchhiker's guide to deep chemical language processing", exploring important aspects of chemical language processing methods in the context of bioactivity prediction. Read more here: doi.org/10.1039/D4DD00…

Happy that our “Hitchhiker’s guide” to the universe of chemical language processing is out Digital Discovery. 🚀 👽 We provide guidelines on how to use deep learning to learn the “chemical language” of bioactivity. 🧪 Spearheaded by the one and only Rıza Özçelik 💪🏻 Molecular Machine Learning

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…

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 🙂

📢 "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 🧵

Are you using generative #DeepLearning for de novo molecule design?🧪 🖥️ Then check out Rıza Özçelik ‘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. 🌴 🐒 👇

Preprint alert! ‼️

"The Jungle of Generative Drug Discovery: Traps, Treasures, and Ways Out" by Rıza Özçelik , Francesca Grisoni Paper: arxiv.org/abs/2501.05457 #machinelearning

Proud of Helena Brinkmann’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… Molecular Machine Learning TU Eindhoven

🔈 New preprint alert!

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! 🧵👇

Great to see our work out in Angewandte Chemie! 🎉 We introduce ‘supramolecular’ language processing to predict co-crystallization — with experimental validation! 🙈 Led by the unstoppable Rebecca Birolo, w/ Rıza Özçelik; TU Eindhoven, Molecular Machine Learning; Università di Torino 💪🏻 onlinelibrary.wiley.com/doi/10.1002/an…

Our DeepCocrystal is now online Angewandte Chemie! 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! 🥼🧪

New paper alert 👇