TODESCHINI ROBERTO

Boldini, D., Ballabio, D., Consonni, V., Todeschini, R., Grisoni, F., Sieber, S. (2024). Effectiveness of molecular fingerprints for exploring the chemical space of natural products. JOURNAL OF CHEMINFORMATICS, 16(1) [10.1186/s13321-024-00830-3]. Detail

Consonni, V., Todeschini, R., Orlandi, M., Ballabio, D. (2024). Kernel-based mapping of reliability in predictions for consensus modelling. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 246(15 March 2024) [10.1016/j.chemolab.2024.105085]. Detail

Consonni, V., Ballabio, D., Todeschini, R. (2023). Chemical space and Molecular Descriptors for QSAR studies. In R. Kunal (a cura di), Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development (pp. 303-327). Elsevier [10.1016/B978-0-443-18638-7.00022-0]. Detail

Valsecchi, C., Grisoni, F., Consonni, V., Ballabio, D., Todeschini, R. (2023). Multitask Learning for Quantitative Structure–Activity Relationships: A Tutorial. In H. Hong (a cura di), Machine Learning and Deep Learning in Computational Toxicology (pp. 199-220). Springer [10.1007/978-3-031-20730-3_8]. Detail

Consonni, V., Todeschini, R., Gosetti, F., Ballabio, D. (2023). Authenticity and Chemometrics Basics. In S. Agriopoulou, M. Tarapoulouzi, T. Varzakas (a cura di), Chemometrics and Authenticity of Foods of Plant Origin (pp. 9-35). CRC Press [10.1201/9781003266167-2]. Detail