Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.
By Alexandru Korotcov, Valery Tkachenko, Daniel P. Russo, Sean Ekins. Molecular Pharmaceutics, 2017
303 citations recorded by the source corpus.
Subfields: Biophysics, Computational Theory and Mathematics, Immunology
View Paper: https://www.semanticscholar.org/paper/e767ea3f3875f096a70bbdac4847436a5e30800e
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In Biophysics
In Computational Theory and Mathematics
- Gisbert Schneider
- Petra Schneider
- D. A. Senthebane
- P. Seele
- Nicholas E. Thomford
- A. Maroyi
- Daniella Munro
- Kevin Dzobo
In Immunology
Citations in the corpus
- Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery (2018, methodology): “Machine learning methods can be used for virtual screening of thousands of compounds allowing the utilisation of data from high throughput screening [170,171].”
- Rethinking drug design in the artificial intelligence era (2019, background): “…to the availability of data for model building is the fact that, in many cases, experiments do not generate data that can be easily translated to a single number like a biochemical dissociation constant ( K d ) or a cellular effective concentration for half-maximum response (EC 50 ) value 51 .”
- Why do tree-based models still outperform deep learning on typical tabular data? (2022, methodology): “…models The most comprehensive comparisons of machine learning algorithms have been published before the advent of new deep learning methods [Caruana and Niculescu-Mizil, 2006, Fernández-Delgado et al., 2014], or on specific problems [Sakr et al., 2017, Korotcov et al., 2017, Uddin et al., 2019].”
- Artificial intelligence in cancer target identification and drug discovery (2022, methodology): “For example, Liu et al. 171 proposed Chemi-Net, which utilizes GCN for ADMET properties prediction.”
- Why do tree-based models still outperform deep learning on tabular data? (2022, unknown): “Comparisons between NNs and tree-based models The most comprehensive comparisons of machine learning algorithms have been published before the advent of new deep learning methods, or on specific problems [Fernández-Delgado et al., 2014, Sakr et al., 2017, Korotcov et al., 2017, Uddin et al., 2019].”