calcoloscientifico:risulati
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| The papers listed here describe results made possible in part by the support of the High Performance Computing facility of the University of Parma (HPC.unipr.it) ]]: | The papers listed here describe results made possible in part by the support of the High Performance Computing facility of the University of Parma (HPC.unipr.it) ]]: | ||
| - | === 2023 === | + | === 2025=== |
| - | * Rabasco, M., & Battiston, P. (2023). Predicting the deterrence effect of tax audits. A machine learning approach. Metroeconomica, | + | |
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| + | === 2024=== | ||
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| + | * Matteo Dubbini, Federico Bonvini, Lorenzo Savi, and Francesco Di Maiolo; Turning on Organic Radical Emitters; The Journal of Physical Chemistry C; DOI: 10.1021/ | ||
| + | * Andrea Landi, D. K. Andrea Phan Huu, and Anna Painelli; From the Fermi Golden Rule to Open Quantum Systems: Basic Concepts on Non-radiative Rates The Journal of Physical Chemistry C; DOI: 10.1021/ | ||
| + | * Bedogni, M.; Di Maiolo, F. " | ||
| + | * Di Maiolo, F.; Phan Huu, D. K. A.; Giavazzi, D.; Landi, A.; Racchi, O.; Painelli A. " | ||
| + | * Perugino F, Pedroni L, Galaverna G, Dall' | ||
| + | * Pedroni L, Perugino F, Magnaghi F, Dall’Asta C, Galaverna G, Dellafiora L. Free fatty acid receptors beyond fatty acids: A computational journey to explore peptides as possible binders of GPR120. Current Research in Food Science. Volume 8, 2024. doi: | ||
| + | * Battiston, P., Gamba, S., Santoro, A. (2024). Machine learning and the optimization of prediction-based policies. Technological Forecasting and Social Change, 199, 123080 | ||
| + | * Bedini A, Elisi GM, Fanini F, Retini M, Scalvini L, Pasquini S, Contri C, Varani K, Spadoni G, Mor M, Vincenzi F, Rivara S. Binding and unbinding of potent melatonin receptor ligands: mechanistic simulations and experimental evidence. J. Pineal Res. 2024 76:e12941 | ||
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| + | === 2023 === | ||
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| + | * Arafet K, Scalvini L, Galvani F, Martí S, Moliner V, Mor M, Lodola A. Mechanistic modeling of Lys745 sulfonylation in EGFR C797S reveals chemical determinants for inhibitor activity and discriminates reversible from irreversible agents. J. Chem. Inf. Model. 2023 63, 1301-1312. | ||
| + | * Galvani F, Pala D, Cuzzolin A, Scalvini L, Lodola A, Mor M, Rizzi A. Unbinding kinetics of muscarinic M3 receptor antagonists explained by metadynamics simulations. J. Chem. Inf. Model. 2023 63, 2842-2856. | ||
| + | * Guidetti L, Zappia A, Scalvini L, Ferrari FR, Giorgio C, Castelli R, Galvani F, Vacondio F, Rivara S, Mor M, Urbinati C, Rusnati M, Tognolini M, Lodola A. Molecular determinants of EphA2 and EphB2 antagonism enable the design of ligands with improved selectivity. J. Chem. Inf. Model. 2023 63, 6900-6911 | ||
| + | * Buoli Comani, V., De Bei, O., Cozzi, M., Marchesani, F., Marchetti, M., Paris, G., Ronda, L., Campanini, B., Luisi, B.F., Bettati, S., Faggiano, S. (2023). Solving the Cryo-EM structure of a complex between human hemoglobin and a bacterial receptor to model new protein-protein interaction inhibitors. In "XXII Giornata della Chimica dell’Emilia Romagna 2023" | ||
| + | | ||
| * Matteo Bedogni, Davide Giavazzi, Francesco Di Maiolo, Anna Painelli Shining Light on Inverted Singlet–Triplet Emitters J. Chem. Theory Comput. (2023) https:// | * Matteo Bedogni, Davide Giavazzi, Francesco Di Maiolo, Anna Painelli Shining Light on Inverted Singlet–Triplet Emitters J. Chem. Theory Comput. (2023) https:// | ||
| * Giavazzi, D.; Di Maiolo, F.; Painelli, A. The fate of molecular excited states: modeling donor-acceptor dyes Phys. Chem. Chem. Phys., 24, 5555-5563 (2022). | * Giavazzi, D.; Di Maiolo, F.; Painelli, A. The fate of molecular excited states: modeling donor-acceptor dyes Phys. Chem. Chem. Phys., 24, 5555-5563 (2022). | ||
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| === 2022 === | === 2022 === | ||
| + | * Elisi GM, Scalvini L, Lodola A, Mor M, Rivara S. Free-energy simulations support a lipophilic binding route for melatonin receptors. J. Chem. Inf. Model. 2022 62, 210-222. | ||
| + | * Galvani F, Scalvini L, Rivara S, Lodola A, Mor M. Mechanistic modeling of monoglyceride lipase covalent modification elucidates the role of leaving group expulsion and discriminates inhibitors with high and low potency. J. Chem. Inf. Model. 2022 62, 2771-2787. | ||
| + | * Mari M, Elisi GM, Bedini A, Lucarini S, Retini M, Lucini V, Scaglione F, Vincenzi F, Varani K, Castelli R, Mor M, Rivara S, Spadoni G. 2-Arylmelatonin analogues: probing the 2-phenyl binding pocket of melatonin MT1 and MT2 receptors. Eur. J. Med. Chem. 2022 243:114762. | ||
| * Gianmarco Lazzini, Luca Romoli, Francesco Fuso, Fluid-driven bacterial accumulation in proximity of laser-textured surfaces, Colloids and Surfaces B: Biointerfaces, | * Gianmarco Lazzini, Luca Romoli, Francesco Fuso, Fluid-driven bacterial accumulation in proximity of laser-textured surfaces, Colloids and Surfaces B: Biointerfaces, | ||
| * Lammi C, Boschin G, Bartolomei M, Arnoldi A, Galaverna G, Dellafiora L. Mechanistic Insights into Angiotensin I-Converting Enzyme Inhibitory Tripeptides to Decipher the Chemical Basis of Their Activity [published online ahead of print, 2022 Sep 8]. J Agric Food Chem. 2022; | * Lammi C, Boschin G, Bartolomei M, Arnoldi A, Galaverna G, Dellafiora L. Mechanistic Insights into Angiotensin I-Converting Enzyme Inhibitory Tripeptides to Decipher the Chemical Basis of Their Activity [published online ahead of print, 2022 Sep 8]. J Agric Food Chem. 2022; | ||
calcoloscientifico/risulati.1701420541.txt.gz · Ultima modifica: 01/12/2023 09:49 da fausto.pagani