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.
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