Philipp Seidl

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Indice H	7	7
i10-index	7	7

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

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Philipp Seidl

Institute for Machine Learning, Johannes Kepler University Linz

Email verificata su ml.jku.at

machine learning drug discovery ML for life sciences


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	546	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	107*	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	53	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	35	2023
Re-evaluating retrosynthesis algorithms with syntheseus K Maziarz, A Tripp, G Liu, M Stanley, S Xie, P Gaiński, P Seidl, ... Faraday Discussions, 2024	13	2024
Hopfield networks is all you need. CoRR abs/2008.02217 (2020) H Ramsauer, B Schäfl, J Lehner, P Seidl, M Widrich, L Gruber, ...	12	2008
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
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	6	2024
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	5	2004
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
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		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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