Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives

By Nathalie Lagarde, Jean-François Zagury, Matthieu Montès. 2015

77 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Organic Chemistry, Pharmacology

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. Izhar Wallach
2. Lucy J. Colwell
3. Michael Dzamba
4. Abraham Heifets
5. Alpha Albert Lee
6. Michael P. Brenner
7. Abhinav Vishnu
8. Garrett B. Goh

In Organic Chemistry

1. Izhar Wallach
2. Michael Dzamba
3. Abraham Heifets

In Pharmacology

1. Izhar Wallach
2. Michael Dzamba
3. Abraham Heifets

Citations in the corpus

• AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery (2015, background): “The dependence on known active ligands also makes it difficult to show that the network is “right for the right reasons”; artifacts in the training data, such as analogue bias, make it very difficult to properly assess accuracy and generalizability [16, 17, 18].”
• Predicting protein–ligand affinity with a random matrix framework (2016, background): “Negative results are seldom reported and the judicious selection of decoys is still a subject of intense research effort [29].”