Comparison of descriptor spaces for chemical compound retrieval and classification

By Nikil Wale, Ian A. Watson, George Karypis. 2006

734 citations recorded by the source corpus.

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

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Citations in the corpus

Learning Convolutional Neural Networks for Graphs (2016, background): “NCI1 and NCI109 are chemical compounds screened for activity against non-small cell lung cancer and ovarian cancer cell lines (Wale & Karypis, 2006).”
Diffusion-Convolutional Neural Networks (2015, background): “Each graph is labeled with whether it is has the ability to suppress or inhibit the growth of a panel of human tumor cell lines, and each node is assigned one of 37 (for NCI1) or 38 (for NCI109) possible labels.”
Discriminative Embeddings of Latent Variable Models for Structured Data (2016, neutral): “NCI1 and NCI109 (Wale et al., 2008) are chemical compounds dataset, while ENZYMES (Borgwardt & Kriegel, 2005) and D&D (Dobson & Doig, 2003) are of proteins.”
Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity (2019, neutral): “7, 8, 9, and 10, for the PTC, WEB, NCI109, and REDDIT12K datasets used in the main paper.”
Pre-training Graph Neural Networks with Kernels (2018, neutral): “NCI1 contains anti-cancer screens for cell lung cancer.”
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (2017, background): “• We reach a competitive level of performance on graph classiﬁcation benchmark NCI1 [39], outperforming other approaches based on deep learning there.”
Propagation kernels: efficient graph kernels from propagated information (2014, background): “nci1 and nci109 are anti-cancer screens, in particular for cell lung cancer and ovarian cancer cell lines, respectively (Wale and Karypis 2006).”
Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings (2015, background): “nci1s is a set of 200 graphs representing a subset of chemical compounds screened for activity against non-small cell lung cancer cell lines [42].”
Matching Node Embeddings for Graph Similarity (2017, methodology): “We last report on the performance of our methods and the baselines.”
On Filter Size in Graph Convolutional Networks (2018, methodology): “In this setting, ﬁve datasets containing biological node-labeled graphs are employed, namely MUTAG [34], PTC [35], NCI1 [36], PROTEINS, and D & D [37].”
Graph Representation Learning via Multi-task Knowledge Distillation (2019, methodology): “For the NCI1 dataset, the Deep WL method still remains the best, probably because the WL kernel is a very good ﬁt for this speciﬁc task.”
Vision GNN: An Image is Worth Graph of Nodes (2022, background): “The GCNs are usually applied on graph data, such as social networks [12], citation networks [43] and biochemical graphs [53].”
Subgraph Matching Kernels for Attributed Graphs (2012, background): “As remarked before (Wale et al., 2008; Costa & De Grave, 2010) kernels using features of diﬀerent size are typically biased towards large features.”
Graph Self-Supervised Learning: A Survey (2021, background): “69 18 2 [14], [89], [97], [100] NCI-1 [143] 4,110 29.”
Fast Neighborhood Subgraph Pairwise Distance Kernel (2010, methodology): “Here we consider the Graph Fragment Kernel (GFK) introduced in (Wale et al., 2008), the Weighted Decomposition Kernel (WDK) by (Menchetti et al.”