Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME.
By Robert C Glem, Andreas Bender, C. H. Arnby, L. Carlsson, S. Boyer, James Smith. 2006
194 citations recorded by the source corpus.
Subfields: 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 Chemistry & Drug Discovery
- Igor V. Tetko
- Peter Ertl
- Jian Tang
- Dougal Maclaurin
- Igor I. Baskin
- Matthias Rupp
- João Aires-de-Sousa
- Artem Cherkasov
Citations in the corpus
- Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information (2011, background): “MolPrint descriptors [51] (2D) are circular fingerprints [60] based on Sybyl mol2 atom types.”
- Modeling, Inference and Optimization With Composable Differentiable Procedures (2016, methodology): “Circular fingerprints [32] are a refinement of the Morgan algorithm [70], designed to encode which substructures are present in a molecule.”
- Edge Attention-based Multi-Relational Graph Convolutional Networks (2018, methodology): “s, 3D ball-andstick, and space-fill models. Most QSAR studies on molecules have been carried out based upon some predefined molecular descriptors or fingerprints, e.g., molecular circular fingerprint [13]. In this work, we present an Edge Attention-based Multi-relational GCN (EAGCN) to predict physi”
- Utilizing graph machine learning within drug discovery and development (2021, methodology): “They demonstrated improved interpretability of the model and predictive superiority over previous approaches which relied on circular fingerprints [181].”