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Multitask Learning for Quantitative Structure–Activity Relationships: A Tutorial

Authors :
Hong, H
Valsecchi, C
Grisoni, F
Consonni, V
Ballabio, D
Todeschini, R
Valsecchi, Cecile
Grisoni, Francesca
Consonni, Viviana
Ballabio, Davide
Todeschini, Roberto
Hong, H
Valsecchi, C
Grisoni, F
Consonni, V
Ballabio, D
Todeschini, R
Valsecchi, Cecile
Grisoni, Francesca
Consonni, Viviana
Ballabio, Davide
Todeschini, Roberto
Publication Year :
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.

Details

Database :
OAIster
Notes :
ELETTRONICO, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1376780976
Document Type :
Electronic Resource