Effectiveness of 2D fingerprints for scaffold hopping.
By Eleanor J. Gardiner, John D. Holliday, C. O'Dowd, Peter Willett. 2011
72 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Geometry and Topology, Safety Research, 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
- Marinka Zitnik
- Jure Leskovec
- Vijay S. Pande
- Bowen Liu
- Weihua Hu
- Joseph Gomes
- Percy Liang
- Gregory A. Landrum
In Geometry and Topology
In Safety Research
In Computational Chemistry & Drug Discovery
- Jure Leskovec
- Bowen Liu
- Joseph Gomes
- Marinka Zitnik
- Weihua Hu
- Percy Liang
- Vijay S. Pande
- Gregory A. Landrum
Citations in the corpus
- Strategies for Pre-training Graph Neural Networks (2019, methodology): “Subset of PubChem BioAssay by applying a refined nearest neighbor analysis, designed for validation of virtual screening techniques (Gardiner et al., 2011).”
- Graph Self-Supervised Learning: A Survey (2021, unknown): “86 2 [10], [16], [70], [75], [101], [108] MUV [149] 93,087 24.”
- Open-source platform to benchmark fingerprints for ligand-based virtual screening (2013, methodology): “Because the BMS definition has been used in studies assessing the scaffold-hopping potential of 2D fingerprints [39,40] it is used here.”