Software for molecular docking: a review
By N. S. Pagadala, K. Syed, Jack Adam Tuszynski. 2017
1,264 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Molecular Biology, Organic Chemistry
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
- Tommi S. Jaakkola
- Regina Barzilay
- Ola Engkvist
- Shigenori Tanaka
- Shuya Nakata
- Yoshiharu Mori
- Bowen Jing
- Gabriele Corso
In Molecular Biology
- Tommi S. Jaakkola
- Regina Barzilay
- Hannes Stärk
- Shuya Nakata
- Shigenori Tanaka
- Yoshiharu Mori
- Bowen Jing
- Gabriele Corso
In Organic Chemistry
- Joseph J. Noh
- Masha Karelina
- Shigenori Tanaka
- Ron O. Dror
- Ola Engkvist
- Marcus Olivecrona
- Hongming Chen
- Thomas Blaschke
Citations in the corpus
- End-to-end protein–ligand complex structure generation with diffusion-based generative models (2022, background): “Despite its success in drug discovery and other applications, correct sampling of binding poses can be limited by poor modeling of protein flexibility [9–11].”
- DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking (2022, background): “Due to the relative rigidity of the protein in the majority of the cases, docking methods typically assume the knowledge of the bound protein structure, and we will follow this assumption [Pagadala et al., 2017].”
- Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model (2020, background): “This deep learning-based approach is particularly useful, since it does not require protein structural information, which can be a bottleneck for identifying drugs targeted for uncharacterized proteins with traditional three-dimensional (3D) structure-based docking approaches [20].”
- The rise of deep learning in drug discovery. (2018, background): “Assessing the interaction between a protein and a ligand is the crucial part of the molecular docking program and a lot of scoring functions were developed either based on forcefields or knowledge from existing protein–ligand complex structures [79].”
- How accurately can one predict drug binding modes using AlphaFold models? (2023, background): “Third, we have not examined performance of the full range of available computational docking methods (Pagadala et al., 2017).”