Are you passionate about the untapped potential of #AI in drug discovery? Then, check out our openings Molecular Machine Learning: 1 PhD & 1 Postdoc positions, funded by European Research Council (ERC)! You will be hosted at the vibrant TU Eindhoven & @ICMStue. See below! 👇 (1/4)

Are you a master student? Eager to learn about #QuantumComputing? Combining optimization (#OptimalTransport) with #MachineLearning & applying to cell biology sounds interesting? Come join us IBM Research for an interdisciplinary thesis! Apply: ibm.biz/msc_qdd Please RT!

Happy two-year anniversary to the Molecular Machine Learning 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 🥂

🚀 Exciting news! Our paper on GT4SD has been published in npj Journals Computational Materials! Learn more about it here: doi.org/10.1038/s41524…. Try it out: GitHub - github.com/GT4SD/gt4sd-co… Demo apps on HF Spaces - huggingface.co/GT4SD

Explore our latest paper on designing catalysts using deep generative models and computational data. Discover how an RNN-VAE can generate new catalyst candidates for the Suzuki coupling. doi.org/10.1039/D2DD00… IBM Research LIAC at EPFL Alain Vaucher Philippe Schwaller (he/him) Teodoro Laino

Very excited our Preprint “Leveraging Infrared Spectroscopy for Automated Structure Elucidation” is out. First of its kind model: Automatic Structure determination from IR spectra! Top-1 Accuracy of 45%! Alain Vaucher Teodoro Laino IBM Research Paper: ibm.biz/ir_to_struc 🧵⬇️

📢📢Our review on structure-based drug discovery with deep learning is now out in ChemBioChem journal! 👇

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Our review on structure-based #drugdiscovery is out! We cover it's different facets, ranging from drug-target interaction prediction and binding site prediction, all the way to de novo drug design. Finally, we look at the challenges ahead 🔮 Molecular Machine Learning #ML #AI #chemistry

📢 If you are attending ACS Fall 2023 in San Francisco and you want to know more about #LanguageModels applied to material design, stop by Nob Hill A - Marriot Marquis at 10:25AM today IBM Research

If you are #ACSFall2023 and want to learn how we can utilize AI to predict the right solvent for a chemical reaction, check out my talk today at 3:10Pm at Nikko II - Hotel Nikko or read the preprint doi.org/10.26434/chemr… IBM Research LIAC at EPFL

Check out our Preprint in ChemRxiv on "Learning the Language of NMR: Structure Elucidation from NMR spectra using Transformer Models". We train models to directly predict the molecular structure from NMRs! Paper: ibm.biz/nmr_to_struc Alain Vaucher Federico Zipoli IBM Research

Ana Ortiz and I just released an exciting preprint on “Machine learning-guided high throughput nanoparticle design” with Roy van der Meel Francesca Grisoni Lorenzo Albertazzi doi.org/10.26434/chemr… Molecular Machine Learning Nanoscopy for Nanomedicine lab #ML #nanomedicine #nanoparticles #drugdiscovery #AI A small 🧵 below👇

📢 Open postdoc position on protein representation learning for drug discovery! 📢 Funded by European Research Council (ERC) 🇪🇺 Join our vibrant team Molecular Machine Learning+TU Eindhoven to push the boundaries of #AI for #drugdiscovery. 💪🏻 Apply before October 31st!👇 jobs.tue.nl/en/vacancy/pos… Please RT 🙂

Super thrilled about the work of Derek van Tilborg on active #deeplearning for drug discovery 💪🏻 We address a broad range of questions on how to use active learning to explore the chemical space.🚀 It turns out active deep learning is quite powerful, even with little data!👇

We have a new preprint on DFT and eigenvalue algorithms: "Hermitian Pseudospectral Shattering, Cholesky, Hermitian Eigenvalues, and Density Functional Theory in Nearly Matrix Multiplication Time". Link to Arxiv: arxiv.org/abs/2311.10459

Interested in Automating Structure Elucidation using AI? Come check out my talk at #ACSFall2024 . Today at 11:35 in Bluebird Ballroom 2G

In "Language models can identify enzymatic binding sites in protein sequences", we identify binding site positions based on SMILES and protein AA sequences. Compared to predictions based on PFAM alignments, we significantly lower the false-positive rate. sciencedirect.com/science/articl…