Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions.

By Z. Deng, C. Chuaqui, Juswinder Singh. Journal of Medicinal Chemistry, 2004

531 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Organic Chemistry, Pharmacology

View Paper: https://www.semanticscholar.org/paper/49094189eef007f0ff8d17d1c8f896788a132da1

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. David B. Ascher
2. Adriano Defini Andricopulo
3. Santiago Garcia-Vallvé
4. Adrià Cereto-Massagué
5. Jürgen Bajorath
6. Miquel Mulero
7. Izhar Wallach
8. Gerard Pujadas

In Organic Chemistry

1. David B. Ascher
2. Jürgen Bajorath
3. Izhar Wallach
4. Adriano Defini Andricopulo
5. Michael Dzamba
6. Abraham Heifets

In Pharmacology

1. Jürgen Bajorath
2. Izhar Wallach
3. Krzysztof Rataj
4. Stefan Mordalski
5. Andrzej J. Bojarski
6. Adriano Defini Andricopulo
7. Michael Dzamba
8. Abraham Heifets

Citations in the corpus

• Fragment-based QSAR strategies in drug design (2010, background): “functional groups) and the putative amino-acid residues of the macromolecular target, shape complementarity and other entropic effects [6,12-14,26].”
• Multiple conformational states in retrospective virtual screening – homology models vs. crystal structures: beta-2 adrenergic receptor case study (2015, methodology): “Our previous study applying Machine Learning (ML) to post-docking analysis used Structural Interaction Fingerprint (SIFt) profiles created upon three different crystalline conformations of receptors [9,10].”
• Arpeggio: A Web Server for Calculating and Visualising Interatomic Interactions in Protein Structures (2017, methodology): “Each pairwise interatomic contact is given a structural interaction fingerprint (SIFt) [26] using an expanded definition of the [15] interaction types.”
• Docking and scoring in virtual screening for drug discovery: methods and applications (2004, background): “Another recent approach generates structural interaction fingerprints (SIFts) that allow pre-screening for potential ligands in databases prior to dockin”
• AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery (2015, background): “Basic structural features can vary from a simple enumeration of atom types to more complex protein-ligand descriptors such as SPLIF [31], SIFt [32], or APIF [33].”
• Molecular fingerprint similarity search in virtual screening. (2015, background): “Structural Interaction Fingerprint (SIFt) is also one of these fingerprints [38].”