| 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 | 42 | 2018 |
| Neural network activation similarity: a new measure to assist decision making in chemical toxicology TEH Allen, AJ Wedlake, E Gelžinytė, C Gong, JM Goodman, S Gutsell, ... Chemical science 11 (28), 7335-7348, 2020 | 20 | 2020 |
| 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 | 20 | 2018 |
| ACEpotentials. jl: A Julia implementation of the atomic cluster expansion WC Witt, C van der Oord, E Gelžinytė, T Järvinen, A Ross, JP Darby, ... The Journal of Chemical Physics 159 (16), 2023 | 11 | 2023 |
| Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecules E Gelžinytė, M Öeren, MD Segall, G Csányi Journal of Chemical Theory and Computation 20 (1), 164-177, 2023 | 4 | 2023 |
| wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows E Gelžinytė, S Wengert, TK Stenczel, HH Heenen, K Reuter, G Csányi, ... The Journal of Chemical Physics 159 (12), 2023 | 4 | 2023 |
| In Silico Approaches to Link Adverse Outcomes to Molecular Initiating Events through AOPs TEH Allen, AJ Wedlake, AM Middleton, MN Grayson, E Gelžinytė, M Folia, ... | | |
