A Neural Device for Searching Direct Correlations between Structures and Properties of Chemical Compounds

By Igor I. Baskin, V. Palyulin, N. Zefirov. 1997

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

1. Gisbert Schneider
2. Stephan Günnemann
3. Johannes Klicpera
4. Paul Wrede
5. Igor V. Tetko
6. David A. Winkler
7. Janek Groß
8. Igor I. Baskin

Citations in the corpus

• Directional Message Passing for Molecular Graphs (2020, background): “GNNs were first proposed in the 90s (Baskin et al., 1997; Sperduti & Starita, 1997) and 00s (Gori et al., 2005; Scarselli et al., 2009).”
• Isometric Transformation Invariant and Equivariant Graph Convolutional Networks (2020, background): “The studies presented by Baskin et al. (1997); Sperduti & Starita (1997); Gori et al. (2005); Scarselli et al. (2008) were the first research works that discussed the concept of GNNs.”
• Diffusion Improves Graph Learning (2019, background): “[6], and the name GNN first appeared in [24, 65].”
• Artificial neural networks for computer-based molecular design. (1998, methodology): “Several hundred di€erent descriptors have been invented to cover the respective context of a SAR, and there seems to be a set of`basic invariants' of molecular graphs which can be calculated directly from the molecular structure of an organic compound as claimed by Baskin et al. (1993, 1995, 1997).”
• A renaissance of neural networks in drug discovery (2016, methodology): “In 1997, the first multilayer ANN with convolutional layers containing shared weights ( ‘ receptors ’ ) and pooling layers ( ‘ collectors ’ ), capable of extracting molecular features from raw data, was reported [85].”