QSAR - How Good Is It in Practice? Comparison of Descriptor Sets on an Unbiased Cross Section of Corporate Data Sets

By P. Gedeck, B. Rohde, C. Bartels. 2006

186 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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. Gregory A. Landrum
2. Sereina Riniker
3. Alpha Albert Lee
4. Kyle Swanson
5. Vijay S. Pande
6. Steven M. Kearnes
7. Jian Tang
8. Minkai Xu

In Materials Chemistry

1. Gregory A. Landrum
2. Sereina Riniker
3. Alpha Albert Lee
4. Vijay S. Pande
5. Steven M. Kearnes
6. Roger A. Sayle
7. David Hoksza
8. Petr Skoda

In Molecular Biology

1. Gregory A. Landrum
2. Sereina Riniker
3. Alpha Albert Lee
4. Kyle Swanson
5. Jian Tang
6. Minkai Xu
7. Tommi S. Jaakkola
8. Regina Barzilay

Citations in the corpus

• Ligand biological activity predicted by cleaning positive and negative chemical correlations (2019, methodology): “In SI Appendix, we show that our method also outperforms other common machine learning methods for the Avalon fingerprint, another descriptor based on a handcrafted set of chemical features (16), showing that the importance of cleaning undersampling noise is independent of descriptor choice.”
• Comparing structural fingerprints using a literature-based similarity benchmark (2016, unknown): “Substructure keys Avalon [28], MACCS.”
• Open-source platform to benchmark fingerprints for ligand-based virtual screening (2013, methodology): “A detailed description of the feature classes is given in Table 1 and the supplementary material of [46].”