In Silico Prediction of Aqueous Solubility: The Solubility Challenge

By M. Hewitt, M. Cronin, S. Enoch, J. Madden, David W. Roberts, John C. Dearden. 2009

100 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. David B. Ascher
2. Douglas E. V. Pires
3. Tom L. Blundell
4. John B. O. Mitchell
5. Carlos H. M. Rodrigues
6. Antoine Daina
7. Vincent Zoete
8. Olivier Michielin

In Materials Chemistry

1. Douglas E. V. Pires
2. David B. Ascher
3. Tom L. Blundell
4. John B. O. Mitchell
5. Carlos H. M. Rodrigues
6. Yang Zhang
7. Andrej Sali
8. Arne Elofsson

In Spectroscopy

1. Tom L. Blundell
2. Douglas E. V. Pires
3. John B. O. Mitchell
4. David B. Ascher
5. A. Pais
6. Tânia F Cova

Citations in the corpus

• An in silico skin absorption model for fragrance materials. (2014, unknown): “6 log units (Hewitt et al., 2009; Wang and Hou, 2011).”
• Machine learning methods in chemoinformatics (2014, background): “16 Properties such as solubility 17,18 and sublimation energy 19–21 are potentially amenable to modeling by either chemoinformatics or theoretical chemistry approaches.”
• pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures (2015, background): “Fluorine atom Count Integer Toxicophore [1-36]c Binary vector Pharmacophore countd Integer vector a Topological polar surface area(23).”