| Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism J Zhang, SD Petersen, T Radivojevic, A Ramirez, A Pérez-Manríquez, ... Nature communications 11 (1), 4880, 2020 | 242 | 2020 |
| Machine learning for metabolic engineering: A review CE Lawson, JM Martí, T Radivojevic, SVR Jonnalagadda, R Gentz, ... Metabolic Engineering 63, 34-60, 2021 | 237 | 2021 |
| A machine learning Automated Recommendation Tool for synthetic biology T Radivojević, Z Costello, K Workman, H Garcia Martin Nature communications 11 (1), 4879, 2020 | 225 | 2020 |
| Opportunities at the intersection of synthetic biology, machine learning, and automation P Carbonell, T Radivojevic, H Garcia Martin ACS synthetic biology 8 (7), 1474-1477, 2019 | 140 | 2019 |
| Perspectives for self-driving labs in synthetic biology HG Martin, T Radivojevic, J Zucker, K Bouchard, J Sustarich, S Peisert, ... Current Opinion in Biotechnology 79, 102881, 2023 | 45 | 2023 |
| Modified hamiltonian monte carlo for bayesian inference T Radivojević, E Akhmatskaya Statistics and Computing 30 (2), 377-404, 2020 | 37 | 2020 |
| Adaptive splitting integrators for enhancing sampling efficiency of modified Hamiltonian Monte Carlo methods in molecular simulation E Akhmatskaya, M Fernández-Pendás, T Radivojević, JM Sanz-Serna Langmuir 33 (42), 11530-11542, 2017 | 23 | 2017 |
| Constant pressure hybrid Monte Carlo simulations in GROMACS M Fernández-Pendás, B Escribano, T Radivojević, E Akhmatskaya Journal of molecular modeling 20, 1-10, 2014 | 23 | 2014 |
| Multiomics data collection, visualization, and utilization for guiding metabolic engineering S Roy, T Radivojevic, M Forrer, JM Marti, V Jonnalagadda, T Backman, ... Frontiers in bioengineering and biotechnology 9, 612893, 2021 | 18 | 2021 |
| Multi-stage splitting integrators for sampling with modified Hamiltonian Monte Carlo methods T Radivojević, M Fernández-Pendás, JM Sanz-Serna, E Akhmatskaya Journal of Computational Physics 373, 900-916, 2018 | 14 | 2018 |
| Enhancing sampling in atomistic simulations of solid-state materials for batteries: a focus on olivine B Escribano, A Lozano, T Radivojević, M Fernández-Pendás, J Carrasco, ... Theoretical Chemistry Accounts 136 (4), 43, 2017 | 14 | 2017 |
| Mix & match hamiltonian monte carlo T Radivojević, E Akhmatskaya arXiv preprint arXiv:1706.04032, 2017 | 10 | 2017 |
| Ergodic transition in a simple model of the continuous double auction T Radivojević, J Anselmi, E Scalas PloS one 9 (2), e88095, 2014 | 8 | 2014 |
| MACAW: an accessible tool for molecular embedding and inverse molecular design V Blay, T Radivojevic, JE Allen, CM Hudson, H Garcia Martin Journal of chemical information and modeling 62 (15), 3551-3564, 2022 | 7 | 2022 |
| Low-traffic limit and first-passage times for a simple model of the continuous double auction E Scalas, F Rapallo, T Radivojević Physica A: Statistical Mechanics and its Applications 485, 61-72, 2017 | 7 | 2017 |
| Enhancing sampling in computational statistics using modified Hamiltonians T Radivojevic | 7 | 2016 |
| Opportunities at the intersection of synthetic biology, machine learning, and automation. ACS Synth. Biol. 8, 1474–1477 P Carbonell, T Radivojevic, H García Martín | 6 | 2019 |
| BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale TWH Backman, C Schenk, T Radivojevic, D Ando, J Singh, JJ Czajka, ... PLoS computational biology 19 (11), e1011111, 2023 | 4 | 2023 |
| Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models J Zhang, SD Petersen, T Radivojevic, A Ramirez, A Pérez, E Abeliuk, ... BioRxiv, 858464, 2019 | 4 | 2019 |
| ART: a machine learning automated recommendation tool for synthetic biology T Radivojevic, Z Costello, HG Martin arXiv preprint, 2019 | 3 | 2019 |
