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Lukas Friedrich
Lukas Friedrich
Principal Scientist Computational Chemistry & Biologics, Merck Healthcare, Darmstadt, Germany
Verified email at merckgroup.com
Title
Cited by
Cited by
Year
De Novo Design of Bioactive Small Molecules by Artificial Intelligence
D Merk, L Friedrich, F Grisoni, G Schneider
Molecular informatics 37 (1-2), 1700153, 2018
3362018
Generative molecular design in low data regimes
M Moret, L Friedrich, F Grisoni, D Merk, G Schneider
Nature Machine Intelligence 2 (3), 171-180, 2020
1442020
Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators
D Merk, F Grisoni, L Friedrich, G Schneider
Communications Chemistry 1 (1), 68, 2018
982018
Ultrahigh-throughput screening enables efficient single-round oxidase remodelling
A Debon, M Pott, R Obexer, AP Green, L Friedrich, AD Griffiths, D Hilvert
Nature Catalysis 2 (9), 740-747, 2019
812019
Mechanism of praziquantel action at a parasitic flatworm ion channel
SK Park, L Friedrich, NA Yahya, CM Rohr, EG Chulkov, D Maillard, ...
Science translational medicine 13 (625), eabj5832, 2021
612021
From Complex Natural Products to Simple Synthetic Mimetics by Computational De Novo Design
L Friedrich, T Rodrigues, CS Neuhaus, P Schneider, G Schneider
Angewandte Chemie International Edition 55 (23), 6789-6792, 2016
492016
Computer-assisted discovery of retinoid X receptor modulating natural products and isofunctional mimetics
D Merk, F Grisoni, L Friedrich, E Gelzinyte, G Schneider
Journal of medicinal chemistry 61 (12), 5442-5447, 2018
422018
Design of natural‐product‐inspired multitarget ligands by machine learning
F Grisoni, D Merk, L Friedrich, G Schneider
ChemMedChem 14 (12), 1129-1134, 2019
292019
Matrix‐based molecular descriptors for prospective virtual compound screening
F Grisoni, D Reker, P Schneider, L Friedrich, V Consonni, R Todeschini, ...
Molecular Informatics 36 (1-2), 1600091, 2017
252017
MELLODDY: cross pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information
W Heyndrickx, L Mervin, T Morawietz, N Sturm, L Friedrich, A Zalewski, ...
232022
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
212023
Industry-scale orchestrated federated learning for drug discovery
M Oldenhof, G Ács, B Pejó, A Schuffenhauer, N Holway, N Sturm, ...
Proceedings of the AAAI Conference on Artificial Intelligence 37 (13), 15576 …, 2023
202023
Scaffold hopping from synthetic RXR modulators by virtual screening and de novo design
D Merk, F Grisoni, L Friedrich, E Gelzinyte, G Schneider
MedChemComm 9 (8), 1289-1292, 2018
202018
Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase‐1 Inhibitors by Automated De Novo Design
L Friedrich, G Cingolani, YH Ko, M Iaselli, M Miciaccia, MG Perrone, ...
Advanced Science 8 (16), 2100832, 2021
172021
Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions
D Boldini, L Friedrich, D Kuhn, SA Sieber
Journal of Cheminformatics 14 (1), 1-13, 2022
82022
Von komplexen Naturstoffen zu synthetisch leicht zugänglichen Mimetika mithilfe von computergestütztem De‐novo‐Design
L Friedrich, T Rodrigues, CS Neuhaus, P Schneider, G Schneider
Angewandte Chemie 128 (23), 6901-6904, 2016
82016
Shape Similarity by Fractal Dimensionality: An Application in the de novo Design of (−)‐Englerin A Mimetics
L Friedrich, R Byrne, A Treder, I Singh, C Bauer, T Gudermann, ...
ChemMedChem 15 (7), 566-570, 2020
72020
Conformal efficiency as a metric for comparative model assessment befitting federated learning
W Heyndrickx, A Arany, J Simm, A Pentina, N Sturm, L Humbeck, L Mervin, ...
Artificial Intelligence in the Life Sciences 3, 100070, 2023
62023
A generalized framework for embedding-based few-shot learning methods in drug discovery
J Schimunek, L Friedrich, D Kuhn, F Rippmann, S Hochreiter, ...
ELLIS Machine Learning for Molecule Discovery Workshop, 2021
62021
Generating customized compound libraries for drug discovery with machine intelligence
M Moret, L Friedrich, F Grisoni, D Merk, G Schneider
ChemRxiv, 10119299/1, 2019
62019
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