Heterogeneous Classifier Fusion for Ligand-Based Virtual Screening: Or, How Decision Making by Committee Can Be a Good Thing

By Sereina Riniker, Nikolas Fechner, Gregory A. Landrum. 2013

46 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. Vijay S. Pande
2. Steven M. Kearnes
3. Abhinav Vishnu
4. Garrett B. Goh
5. Nathan Oken Hodas
6. Charles Siegel
7. Kyle Swanson
8. Regina Barzilay

In Materials Chemistry

1. Vijay S. Pande
2. Steven M. Kearnes
3. Nathan Oken Hodas
4. Abhinav Vishnu
5. Garrett B. Goh
6. Charles Siegel
7. Kyle Swanson
8. Frank No'e

In Molecular Biology

1. Vijay S. Pande
2. Steven M. Kearnes
3. Frank No'e
4. Hao Wu
5. Cecilia Clementi
6. Brooke E. Husic
7. Chaopeng Shen
8. John D. Chodera

Citations in the corpus

• ROCS-derived features for virtual screening (2016, methodology): “To increase confidence in our results, we trained models using ROCS-derived features on two additional dataset collections: the maximum unbiased validation (MUV) datasets of [22] and a group of benchmarking datasets derived from ChEMBL [19, 20], both of which were specifically designed for the valid”