Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships

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Strategies for Pre-training Graph Neural Networks (2019, background): “Several key studies (Xu et al., 2017; Ching et al., 2018; Wang et al., 2019) have shown that successful transfer learning is not only a matter of increasing the size of a labeled dataset.”
Development and evaluation of a deep learning model for protein–ligand binding affinity prediction (2017, unknown): “…, 2015; Kearnes et al. , 2016; Jastrz¸ebski et al. , 2016; Segler et al. , 2017; Olivecrona et al. , 2017; G´omez-Bombarelli et al. , 2017; Lenselink et al. , 2017; Xu et al. , 2017; Ramsundar et al. , 2017; Wallach et al. , 2015; Ragoza et al. , 2016; Pereira et al. , 2016; Gomes et al. , 2017).”
Machine learning and feature selection for drug response prediction in precision oncology applications (2018, background): “2017) and compound-target interaction prediction (Ma et al. 2015; Xu et al. 2017) that when the feature spaces are large enough, deeplearning machine learning models can learn the most predictive signal from such “big data,” without the need of any processing of filtering steps, hence providing oppo”
Pre-training Graph Neural Networks (2019, background): “This phenomenon is referred to as negative transfer [35, 43], which poses a signiﬁcant problem when deploying pre-trained models to real world applications, and has been previously observed for multi-task learning of molecular property prediction tasks [39, 40, 54, 22].”