Is Multitask Deep Learning Practical for Pharma?

By Bharath Ramsundar, Bowen Liu, Zhenqin Wu, A. Verras, M. Tudor, Robert P. Sheridan, Vijay S. Pande. 2017

241 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Materials Chemistry, Medical & Health AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jure Leskovec
2. Marinka Zitnik
3. William L. Hamilton
4. Weihua Hu
5. Percy Liang
6. Bowen Liu
7. Vijay S. Pande
8. Joseph Gomes

In Computational Theory and Mathematics

1. Marinka Zitnik
2. Jure Leskovec
3. Kyle Swanson
4. Bowen Liu
5. Weihua Hu
6. Ola Engkvist
7. Joseph Gomes
8. Percy Liang

In Materials Chemistry

1. Jure Leskovec
2. Kyle Swanson
3. Ola Engkvist
4. Hongming Chen
5. Marinka Zitnik
6. Marcus Olivecrona
7. Thomas Blaschke
8. Bowen Liu

In Medical & Health AI

1. Marinka Zitnik
2. Kyle Swanson
3. Jure Leskovec
4. Percy Liang

Citations in the corpus

• 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).”
• 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].”
• Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, result): “Moreover, in comparison with AI and ML algorithms, DL provides a much more flexible architecture to create a neural network for a specific problem [477–480].”
• Chemi-net: a graph convolutional network for accurate drug property prediction (2018, background): “The potential application of MT-DNNs in pharmaceutical drug discovery is reviewed by Ramsundar et al. 10 .”
• The rise of deep learning in drug discovery. (2018, result): “Ramsundar et al . carried out a systematic study [38] to build multitask DNNs and compare their performance with single-task DNN models.”
• Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “Deep learning applications in drug discovery and repurposing are starting to emerge, and already show high potential in many tasks including virtual screening, ADME/Tox properties prediction, novel drug-target interactions prediction [8, 28-32].”
• Message passing neural networks for molecular property prediction (2019, methodology): “The models in [46] include MPNN [20], Weave [22], GraphConv, kernel ridge regression, gradient boosting [19], random forest [6], logistic regression [12], directed acyclic graph models [29], support vector machines [11], Deep Tensor Neural Networks [40], multitask networks [30], bypass networks [36]”