eHiTS: a new fast, exhaustive flexible ligand docking system.

By Z. Zsoldos, D. Reid, Anikó Simon, S. Sadjad, A. Peter Johnson. 2007

257 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Immunology, Organic Chemistry, 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. Octavian-Eugen Ganea
4. Hannes Stärk
5. Lagnajit Pattanaik
6. Wengong Jin
7. José Miguel Hernández-Lobato
8. Shitong Luo

In Computational Chemistry & Drug Discovery

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Hannes Stärk
4. Octavian-Eugen Ganea

Citations in the corpus

• TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, neutral): “But since predicted distance matrix contains noise, we take a numerical approach [46, 48].”
• EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, background): “It has previously been assumed that the most flexible parts of drug-like molecules are rotatable bonds, while local atomic structures (LAS) (bond lengths and adjacent bond angles) or small rings are mostly rigid (Trott & Olson, 2010; Zsoldos et al., 2007; Huang, 2018; M´endez-Lucio et al., 2021).”
• Drug Discovery Using Chemical Systems Biology: Repositioning the Safe Medicine Comtan to Treat Multi-Drug and Extensively Drug Resistant Tuberculosis (2009, unknown): “2 [41] and Surflex2.”
• Protein-ligand docking in the new millennium--a retrospective of 10 years in the field. (2013, unknown): “eHiTS Canada | UK 2006 [148]”