Multitask learning allows to model multiple tasks simultaneously through information sharing. In the context of quantitative structure activity relationships and computational toxicology, multitask learning is gaining more and more interest, owed to its potential to improve the predictive performance of underrepresented tasks and to predict the multi-property profile of molecules. In this chapter, after introducing the multitask problem formulation, we present a hands-on tutorial on multitask neural networks.

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].

Multitask Learning for Quantitative Structure–Activity Relationships: A Tutorial

Valsecchi, Cecile
;
Consonni, Viviana;Ballabio, Davide;Todeschini, Roberto
2023

Abstract

Multitask learning allows to model multiple tasks simultaneously through information sharing. In the context of quantitative structure activity relationships and computational toxicology, multitask learning is gaining more and more interest, owed to its potential to improve the predictive performance of underrepresented tasks and to predict the multi-property profile of molecules. In this chapter, after introducing the multitask problem formulation, we present a hands-on tutorial on multitask neural networks.
Capitolo o saggio
chemometrics; neural networks; QSAR
English
Machine Learning and Deep Learning in Computational Toxicology
Hong, H
8-feb-2023
2023
9783031207297
Springer
199
220
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/403342
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