| Drug–membrane permeability across chemical space R Menichetti, KH Kanekal, T Bereau ACS central science 5 (2), 290-298, 2019 | 102 | 2019 |
| An information-theory-based approach for optimal model reduction of biomolecules M Giulini, R Menichetti, MS Shell, R Potestio Journal of chemical theory and computation 16 (11), 6795-6813, 2020 | 57 | 2020 |
| In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force R Menichetti, KH Kanekal, K Kremer, T Bereau The Journal of Chemical Physics 147 (12), 2017 | 43 | 2017 |
| From system modeling to system analysis: The impact of resolution level and resolution distribution in the computer-aided investigation of biomolecules M Giulini, M Rigoli, G Mattiotti, R Menichetti, T Tarenzi, R Fiorentini, ... Frontiers in Molecular Biosciences 8, 676976, 2021 | 37 | 2021 |
| Molecular dynamics trajectories for 630 coarse-grained drug-membrane permeations C Hoffmann, A Centi, R Menichetti, T Bereau Scientific Data 7 (1), 51, 2020 | 32 | 2020 |
| Efficient potential of mean force calculation from multiscale simulations: solute insertion in a lipid membrane R Menichetti, K Kremer, T Bereau Biochemical and biophysical research communications 498 (2), 282-287, 2018 | 21 | 2018 |
| Controlled exploration of chemical space by machine learning of coarse-grained representations C Hoffmann, R Menichetti, KH Kanekal, T Bereau Physical Review E 100 (3), 033302, 2019 | 19 | 2019 |
| A journey through mapping space: characterising the statistical and metric properties of reduced representations of macromolecules R Menichetti, M Giulini, R Potestio The European Physical Journal B 94, 1-26, 2021 | 16 | 2021 |
| Coarse-graining polymer solutions: A critical appraisal of single-and multi-site models G D’Adamo, R Menichetti, A Pelissetto, C Pierleoni The European Physical Journal Special Topics 224 (12), 2239-2267, 2015 | 16 | 2015 |
| A deep graph network–enhanced sampling approach to efficiently explore the space of reduced representations of proteins F Errica, M Giulini, D Bacciu, R Menichetti, A Micheli, R Potestio Frontiers in Molecular Biosciences 8, 637396, 2021 | 15 | 2021 |
| Thermodynamics of star polymer solutions: A coarse-grained study R Menichetti, A Pelissetto, F Randisi The Journal of chemical physics 146 (24), 2017 | 12 | 2017 |
| Revisiting the Meyer-Overton rule for drug-membrane permeabilities R Menichetti, T Bereau Molecular Physics 117 (20), 2900-2909, 2019 | 10 | 2019 |
| Comparing different coarse-grained potentials for star polymers R Menichetti, A Pelissetto The Journal of Chemical Physics 138 (12), 2013 | 6 | 2013 |
| Integral equation analysis of single-site coarse-grained models for polymer–colloid mixtures R Menichetti, G D’Adamo, A Pelissetto, C Pierleoni Molecular Physics 113 (17-18), 2629-2642, 2015 | 3 | 2015 |
| EXCOGITO, an Extensible Coarse-Graining Toolbox for the Investigation of Biomolecules by Means of Low-Resolution Representations M Giulini, R Fiorentini, L Tubiana, R Potestio, R Menichetti Journal of Chemical Information and Modeling, 2024 | 1 | 2024 |
| Coarse-grained Mori-Zwanzig dynamics in a time-non-local stationary-action framework P Luchi, R Menichetti, G Lattanzi, R Potestio arXiv preprint arXiv:2202.10756, 2022 | 1 | 2022 |
| Accelerating the identification of informative reduced representations of proteins with deep learning for graphs F Errica, M Giulini, D Bacciu, R Menichetti, A Micheli, R Potestio arXiv preprint arXiv:2007.08658, 2020 | 1 | 2020 |
| Investigating Drug-Membrane Permeability across Chemical Compound Space using High-Throughput Coarse-Grained Simulations R Menichetti, KH Kanekal, T Bereau Biophysical Journal 116 (3), 318a, 2019 | 1 | 2019 |
| Density of states in neural networks: an in-depth exploration of learning in parameter space M Mele, R Menichetti, A Ingrosso, R Potestio arXiv preprint arXiv:2409.18683, 2024 | | 2024 |
| A multi-scale analysis of the CzrA transcription repressor highlights the allosteric changes induced by metal ion binding M Rigoli, R Potestio, R Menichetti arXiv preprint arXiv:2409.03584, 2024 | | 2024 |
Raffaello PotestioDepartment of Physics, University of TrentoEmail verificata su unitn.it
