A renaissance of neural networks in drug discovery

By Igor I. Baskin, David A. Winkler, Igor V. Tetko. 2016

213 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Oncology, Pharmacology

How researchers see this paper

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

In Computational Theory and Mathematics

1. Ola Engkvist
2. Hongming Chen
3. Gisbert Schneider
4. Marcus Olivecrona
5. Thomas Blaschke
6. Marwin H. S. Segler
7. Lauv Patel
8. David Wayne Ussery

In Oncology

1. Gisbert Schneider

In Pharmacology

1. Gisbert Schneider
2. Jinbo Xu
3. Sean Ekins
4. S. Joshua Swamidass

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Neural networks have a long history in this domain [18,21], and the 2012 Merck Molecular Activity Challenge on Kaggle generated substantial excitement about the potential for high-parameter deep learning approaches.”
• Machine Learning Methods in Drug Discovery (2020, background): “A generative DNN can create novel chemical compounds from existing libraries and training sets (Figure 3a); while, a predictive DNN can predict the chemical attributes of the novel compounds [71,72].”
• The rise of deep learning in drug discovery. (2018, background): “Recently, several reviews on DL applications in computational chemistry and life sciences have been published [13–18].”
• Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. (2017, methodology): “Limited efforts to use deep learning for pharmaceutical applications suggest a need for further exploration to access its utility for cheminformatics compared with other methods 35 .”
• Automating drug discovery (2017, background): “Several heterogeneous deep-learning systems with high prediction accuracies have been developed for drug–target association, drug repurposing opportunities and target identification, among other tasks 202,206,207,208 . and software 180,181 .”
• 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].”