Comparison of Random Forest and Pipeline Pilot Naïve Bayes in Prospective QSAR Predictions

By Bin Chen, Robert P. Sheridan, Viktor Hornak, J. Voigt. 2012

103 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Chemistry & Drug Discovery

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. Marinka Zitnik
2. Jure Leskovec
3. Bowen Liu
4. Weihua Hu
5. Joseph Gomes
6. Percy Liang
7. Vijay S. Pande
8. Chaopeng Shen

In Molecular Biology

1. Marinka Zitnik
2. Jure Leskovec
3. Weihua Hu
4. Bowen Liu
5. Percy Liang
6. Joseph Gomes
7. Vijay S. Pande
8. Petar Velickovic

In Computational Chemistry & Drug Discovery

1. Jure Leskovec
2. Bowen Liu
3. Joseph Gomes
4. Marinka Zitnik
5. Weihua Hu
6. Percy Liang
7. Vijay S. Pande
8. Chaopeng Shen

Citations in the corpus

• Strategies for Pre-training Graph Neural Networks (2019, neutral): “Prior work has shown that this scaffold split provides a more realistic estimate of model performance in prospective evaluation compared to random split (Chen et al., 2012; Sheridan, 2013).”
• Pre-training Graph Neural Networks (2019, background): “Prior work has shown that this scaffold split provides a more realistic estimate of model performance in prospective evaluation compared to random split [6, 45].”