Philipp Seidl

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Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Günter KlambauerProf., LIT AI Lab & Institute for Machine Learning, Johannes Kepler UniversityVerified email at ml.jku.at
Johannes LehnerJohannes Kepler Universität LinzVerified email at ml.jku.at
Michael WidrichMachine Learning Scientist @ FreenomeVerified email at freenome.com
Bernhard SchäflPostdoctoral Research Scientist, University of Natural Resources and Life Sciences, Vienna (BOKU)Verified email at ml.jku.at
Johannes BrandstetterJohannes Kepler University (JKU) LinzVerified email at ml.jku.at
Philipp RenzJKU LinzVerified email at ml.jku.at
Johannes SchimunekJohannes Kepler University Linz - ELLIS Unit at the LIT AI LabVerified email at ml.jku.at
Hubert RamsauerResearch Assistant at Institute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Marwin SeglerMicrosoft Research AI for ScienceVerified email at microsoft.com
Andreu VallPumas-AI, Inc.Verified email at pumas.ai
Jörg Kurt WegnerDirector, In Silico Discovery, Janssen R&D, Cambridge, MA, USVerified email at its.jnj.com
Elisabeth RumetshoferInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Markus HofmarcherInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Andreas MayrInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at

Philipp Seidl

Institute for Machine Learning, Johannes Kepler University Linz

Verified email at ml.jku.at

machine learning drug discovery ML for life sciences


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Hopfield networks is all you need H Ramsauer, B Schäfl, J Lehner, P Seidl, M Widrich, T Adler, L Gruber, ... arXiv preprint arXiv:2008.02217, 2020	615	2020
Improving few-and zero-shot reaction template prediction using modern hopfield networks P Seidl, P Renz, N Dyubankova, P Neves, J Verhoeven, JK Wegner, ... Journal of chemical information and modeling 62 (9), 2111-2120, 2022	113*	2022
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks M Hofmarcher, A Mayr, E Rumetshofer, P Ruch, P Renz, J Schimunek, ... arXiv preprint arXiv:2004.00979, 2020	63	2020
Enhancing activity prediction models in drug discovery with the ability to understand human language P Seidl, A Vall, S Hochreiter, G Klambauer International Conference on Machine Learning, 30458-30490, 2023	57	2023
Context-enriched molecule representations improve few-shot drug discovery J Schimunek, P Seidl, L Friedrich, D Kuhn, F Rippmann, S Hochreiter, ... arXiv preprint arXiv:2305.09481, 2023	37	2023
Re-evaluating retrosynthesis algorithms with syntheseus K Maziarz, A Tripp, G Liu, M Stanley, S Xie, P Gaiński, P Seidl, ... Faraday Discussions 256, 568-586, 2025	21	2025
A community effort in SARS‐CoV‐2 drug discovery J Schimunek, P Seidl, K Elez, T Hempel, T Le, F Noé, S Olsson, L Raich, ... Molecular Informatics 43 (1), e202300262, 2024	8	2024
Using emergency department triage for machine learning-based admission and mortality prediction T Tschoellitsch, P Seidl, C Böck, A Maletzky, P Moser, S Thumfart, ... European journal of emergency medicine: official journal of the European …, 2023	7	2023
Supervised machine learning classification for short straddles on the S&P500 A Brunhuemer, L Larcher, P Seidl, S Desmettre, J Kofler, G Larcher Risks 10 (12), 235, 2022	7	2022
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks. 2020 M Hofmarcher, A Mayr, E Rumetshofer, P Ruch, P Renz, J Schimunek, ... DOI: https://doi. org/10.2139/ssrn 3561442, 2004	7	2004
Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences N Schmidinger, L Schneckenreiter, P Seidl, J Schimunek, PJ Hoedt, ... arXiv preprint arXiv:2411.04165, 2024	1	2024
Potential predictors for deterioration of renal function after transfusion T Tschoellitsch, P Moser, A Maletzky, P Seidl, C Böck, T Roland, ... Anesthesia & Analgesia 138 (3), 645-654, 2024	1	2024
Machine learning prediction of unexpected readmission or death after discharge from intensive care: A retrospective cohort study T Tschoellitsch, A Maletzky, P Moser, P Seidl, C Böck, TT Mahečić, ... Journal of clinical anesthesia 99, 111654, 2024		2024
Multimodal Contrastive Learning for Drug Discovery P Seidl Institute for Machine Learning, 2024		2024
Mitigating Model Bias with the BERT Transformer Architecture for the Detection of Toxic Comments P Seidl Institute for Machine Learning, 2019		2019
Cortical Fatigue in Stroke Patients P Seidl Department of Medical Engineering, 2017		2017

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