Solubility Challenge: Can You Predict Solubilities of 32 Molecules Using a Database of 100 Reliable Measurements?

By A. Llinàs, Robert C. Glen, J. Goodman. 2008

184 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. Lucy J. Colwell
4. Tom L. Blundell
5. Alpha Albert Lee
6. Michael P. Brenner
7. Carlos H. M. Rodrigues
8. Yoochan Myung

In Materials Chemistry

1. Douglas E. V. Pires
2. David B. Ascher
3. Lucy J. Colwell
4. Tom L. Blundell
5. Alpha Albert Lee
6. Michael P. Brenner
7. Carlos H. M. Rodrigues
8. Yang Zhang

In Spectroscopy

1. Lucy J. Colwell
2. Tom L. Blundell
3. Douglas E. V. Pires
4. Alpha Albert Lee
5. Michael P. Brenner
6. David B. Ascher

Citations in the corpus

• 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).”
• Predicting protein–ligand affinity with a random matrix framework (2016, methodology): “Secondly, relating our algorithm to an interaction energy provides a way to extend our method to regression problems, such as predicting solubility [40].”