Ambrosetti, Francesco

Computational approaches to therapeutic antibody design: established methods and emerging trends

Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics

Monoclonal and Polyclonal Antibodies Researchvaccines and immunoinformatics approaches

Cited by 219

DeepRank: a deep learning framework for data mining 3D protein-protein interfaces

Renaud, N., Geng, C., Georgievska, S., Ambrosetti, F., Ridder, L., Marzella, D. F., Réau, M. F., Bonvin, A. M. J. J., & Xue, L. C. (2021) · Nature Communications

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 125

Blind prediction of homo- and hetero-protein complexes: the CASP13-CAPRI experiment

Lensink, M. F., Brysbaert, G., Nadzirin, N., Velankar, S., Chaleil, R. A. G., Gerguri, T., Bates, P. A., Laine, E., Carbone, A., Grudinin, S., et al. (2019) · Proteins Structure Function and Bioinformatics

Protein Structure and DynamicsEnzyme Structure and Function

Cited by 122

A study of novel exploratory tools, digital technologies, and central nervous system biomarkers to characterize unipolar depression

Sverdlov, O., Curcic, J., Hannesdottir, K., Gou, L., De Luca, V., Ambrosetti, F., Zhang, B., Praestgaard, J., Vallejo, V., Dolman, A., et al. (2021) · Frontiers in Psychiatry

Digital Mental Health InterventionsMental Health Research Topics

Cited by 34

Information-driven modelling of antibody–antigen complexes

Ambrosetti, F., Jiménez-García, B., Roel-Touris, J., & Bonvin, A. M. J. J. (2019) · SSRN Electronic Journal

Computational Drug Discovery MethodsProtein Structure and Dynamics

Cited by 2