Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

By Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius ter Laak, Detlev Sülzle, Ursula Ganzer, Nikolaus Heinrich, K. Müller. 2007

59 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Spectroscopy

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. Pierre Baldi
2. Gianluca Pollastri
3. Alessandro Lusci
4. Jianlin Cheng
5. Kyle Swanson
6. Chaopeng Shen
7. John B. O. Mitchell
8. Arlo Z. Randall

In Materials Chemistry

1. Pierre Baldi
2. Gianluca Pollastri
3. Jianlin Cheng
4. Kyle Swanson
5. Chaopeng Shen
6. John B. O. Mitchell
7. Jinbo Xu
8. Nathan Oken Hodas

In Spectroscopy

1. Pierre Baldi
2. John B. O. Mitchell
3. Kyle Swanson
4. Chaopeng Shen
5. Abhinav Vishnu
6. Garrett B. Goh
7. Nathan Oken Hodas
8. Patrick F. Riley

Citations in the corpus

• Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules (2013, background): “Schröter et al, 71 for instance, state that predictions of aqueous solubility for molecules whose structure falls outside the DOA are generally poor.”