Extended-Connectivity Fingerprints

By David Rogers, M. Hahn. 2010

6,516 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. Tommi S. Jaakkola
2. Regina Barzilay
3. Jure Leskovec
4. Abhinav Vishnu
5. Garrett B. Goh
6. Nathan Oken Hodas
7. Charles Siegel
8. Kyle Swanson

In Molecular Biology

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Charles Siegel
4. Garrett B. Goh
5. Nathan Oken Hodas
6. Abhinav Vishnu
7. Jure Leskovec
8. Rex Ying

In Computational Chemistry & Drug Discovery

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Kyle Swanson
4. Abhinav Vishnu
5. Garrett B. Goh
6. Nathan Oken Hodas
7. Wengong Jin
8. Charles Siegel

Citations in the corpus

• Convolutional Networks on Graphs for Learning Molecular Fingerprints (2015, background): “The state of the art in molecular fingerprints are extended-connectivity circular fingerprints (ECFP) [21].”
• Neural Message Passing for Quantum Chemistry (2017, methodology): “To date, most research applying machine learning to chemistry tasks (Hansen et al., 2015; Huang & von Lilienfeld, 2016; 1Google Brain 2Google 3Google DeepMind.”
• Strategies for Pre-training Graph Neural Networks (2019, background): “This, however, can be sub-optimal for graph-level prediction tasks, where capturing structural similarity of local neighborhoods is often more important than capturing the positional information of nodes within a graph (You et al., 2019; Rogers & Hahn, 2010; Yang et al., 2014).”
• Interpreting linear support vector machine models with heat map molecule coloring (2011, background): “Common molecular encodings that generate interpretable features are radial atom environments [3], depth first search paths [15], or extended connectivity fingerprints (ECFP) [16].”
• Molecular graph convolutions: moving beyond fingerprints (2016, background): “One of the most common representations in this class is extended-connectivity fingerprints (ECFP), also referred to as circular or Morgan fingerprints (Rogers and Hahn, 2010).”
• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (2018, background): “The diversity of a set of molecules is defined as the average pairwise Tanimoto distance between the Morgan fingerprints [33] of the molecules.”
• Distributed Representation of Chemical Fragments (2018, background): “Traditionally, cheminformatics algorithms treat fragments as discrete symbols 7,8 , as arbitrary indices in a list of unique fragments and provide no clue about possible relationships between individual fragments (Figure 1a).”
• Learning Graph-Level Representation for Drug Discovery (2017, methodology): “(Duvenaud et al. 2015) propose graph convolutional network that operates directly on graph, which could learn better representations than conventional method like ECFP.”
• Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “Traditional machine learning approaches relied on preprocessing the graph into a feature vector, such as a fixed-width bit vector fingerprint [307].”
• Massively Multitask Networks for Drug Discovery (2015, methodology): “We used extended connectivity fingerprints (ECFP4) (Rogers & Hahn, 2010) generated by RDKit (Landrum) to featurize each molecule.”
• Learning Multimodal Graph-to-Graph Translation for Molecular Optimization (2018, methodology): “We measure the molecular similarity by computing Tanimoto similarity over Morgan fingerprints (Rogers & Hahn, 2010).”