| On the Integration of In Silico Drug Design Methods for Drug Repurposing E March-Vila, L Pinzi, N Sturm, A Tinivella, O Engkvist, H Chen, G Rastelli Frontiers in pharmacology 8, 298, 2017 | 189 | 2017 |
| Structural insights into the molecular basis of the ligand promiscuity N Sturm, J Desaphy, RJ Quinn, D Rognan, E Kellenberger Journal of chemical information and modeling 52 (9), 2410-2421, 2012 | 68 | 2012 |
| Industry-scale application and evaluation of deep learning for drug target prediction N Sturm, A Mayr, T Le Van, V Chupakhin, H Ceulemans, J Wegner, ... Journal of Cheminformatics 12, 1-13, 2020 | 38 | 2020 |
| Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability O Laufkötter, N Sturm, J Bajorath, H Chen, O Engkvist Journal of cheminformatics 11, 1-14, 2019 | 35 | 2019 |
| Comparison of chemical structure and cell morphology information for multitask bioactivity predictions MA Trapotsi, LH Mervin, AM Afzal, N Sturm, O Engkvist, IP Barrett, ... Journal of chemical information and modeling 61 (3), 1444-1456, 2021 | 29 | 2021 |
| Application of bioactivity profile-based fingerprints for building machine learning models N Sturm, J Sun, Y Vandriessche, A Mayr, G Klambauer, L Carlsson, ... Journal of Chemical Information and Modeling 59 (3), 962-972, 2018 | 28 | 2018 |
| Identification of Compounds That Interfere with High‐Throughput Screening Assay Technologies L David, J Walsh, N Sturm, I Feierberg, JWM Nissink, H Chen, J Bajorath, ... ChemMedChem 14 (20), 1795-1802, 2019 | 26 | 2019 |
| Splitting chemical structure data sets for federated privacy-preserving machine learning J Simm, L Humbeck, A Zalewski, N Sturm, W Heyndrickx, Y Moreau, ... Journal of cheminformatics 13, 1-14, 2021 | 23 | 2021 |
| MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information J. Chem. Inf. Model., 2023 | 21 | 2023 |
| 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 | 20 | 2023 |
| Similarity between flavonoid biosynthetic enzymes and flavonoid protein targets captured by three-dimensional computing approach N Sturm, RJ Quinn, E Kellenberger Planta Medica 81 (06), 467-473, 2015 | 14 | 2015 |
| Structural searching of biosynthetic enzymes to predict protein targets of natural products N Sturm, RJ Quinn, E Kellenberger Planta Medica 84 (05), 304-310, 2018 | 9 | 2018 |
| Don’t overweight weights: Evaluation of weighting strategies for multi-task bioactivity classification models L Humbeck, T Morawietz, N Sturm, A Zalewski, S Harnqvist, W Heyndrickx, ... Molecules 26 (22), 6959, 2021 | 7 | 2021 |
| 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 | 6 | 2023 |
| Exploration and comparison of the geometrical and physicochemical properties of an αc allosteric pocket in the structural kinome N Sturm, A Tinivella, G Rastelli Journal of Chemical Information and Modeling 58 (5), 1094-1103, 2018 | 6 | 2018 |
| Comparing atom-based with residue-based descriptors in predicting binding site similarity: do backbone atoms matter? N Sturm, D Rognan, RJ Quinn, E Kellenberger Future Medicinal Chemistry 8 (15), 1871-1885, 2016 | 4 | 2016 |
| Multitask bioactivity predictions using structural chemical and cell morphology information MA Trapotsi, I Barrett, L Mervin, AM Afzal, N Sturm, O Engkvist, A Bender | 3 | 2020 |
| Prediction of Small-Molecule Developability Using Large-Scale In Silico ADMET Models M Beckers, N Sturm, F Sirockin, N Fechner, N Stiefl Journal of medicinal chemistry 66 (20), 14047-14060, 2023 | 2 | 2023 |
| Characterization of natural product biological imprints for computer-aided drug design applications N Sturm Strasbourg, 2015 | | 2015 |
