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Philipp Renz
Philipp Renz
JKU Linz
Bestätigte E-Mail-Adresse bei ml.jku.at
Titel
Zitiert von
Zitiert von
Jahr
Fréchet ChemNet distance: a metric for generative models for molecules in drug discovery
K Preuer, P Renz, T Unterthiner, S Hochreiter, G Klambauer
Journal of chemical information and modeling 58 (9), 1736-1741, 2018
3022018
On failure modes in molecule generation and optimization
P Renz, D Van Rompaey, JK Wegner, S Hochreiter, G Klambauer
Drug Discovery Today: Technologies, 2020
106*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
92*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
592020
Understanding the effects of dataset characteristics on offline reinforcement learning
K Schweighofer, M Hofmarcher, MC Dinu, P Renz, A Bitto-Nemling, ...
arXiv preprint arXiv:2111.04714, 2021
172021
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
52004
Uncertainty estimation methods to support decisionmaking in early phases of drug discovery
P Renz, S Hochreiter, G Klambauer
Workshop on Safety and Robustness in Decisionmaking, 2019
22019
Low-count time series anomaly detection
P Renz, K Cutajar, N Twomey, GKC Cheung, H Xie
2023 IEEE 33rd International Workshop on Machine Learning for Signal …, 2023
2023
Data Driven Molecule Generation Using Deep Learning/submitted by Philipp Renz
P Renz
Universität Linz, 2018
2018
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