Neural networks in chemistry and drug design
By Jure Zupan, J. Gasteiger. 1999
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Ron Wehrens
- M. Cámara
- M. D. De Zan
- J. Havel
- Luciana Vera Candioti
- Héctor C. Goicoechea
- Alberto López
- Lutgarde M. C. Buydens
Citations in the corpus
- MoleculeNet: a benchmark for molecular machine learning (2017, neutral): “linical trials for toxicity reasons.58,59 The dataset includes two classication tasks for 1491 drug compounds with known chemical structures: (1) clinical trial toxicity (or absence of toxicity) and (2) FDA approval status. List of FDA-approved drugs are compiled from the SWEETLEAD database,60 and l”
- Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks (2017, background): “Eventually, success in the wet lab will determine if the new wave[21] of neural networks will prevail.”
- Artificial intelligence in drug discovery and development (2020, unknown): “According to the McKinsey Global Institute, the rapid advances in AI-guided automation will be likely to completely change the work culture of society [5,6].”
- Self- and Super-organizing Maps in R: The kohonen Package (2007, background): “Note that in this approach – in chemistry applications often called a Counter-Propagation Network (Zupan and Gasteiger 1999) – the dependent variable Y does not influence the mapping.”
- Experimental design and multiple response optimization. Using the desirability function in analytical methods development. (2014, methodology): “The socalled multilayer feed-forward networks [30,31] or multi-layer perceptron (MLP) networks are often used for prediction as well as for classification.”
- Artificial neural networks in medical diagnosis (2013, methodology): “However, the most commonly used is back propagation (Zupan and Gasteiger 1999; Ahmed 2005).”