Structure-based drug design to augment hit discovery.

By Subha Kalyaanamoorthy, Yi-Ping Phoebe Chen. 2011

260 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Organic Chemistry, Pharmacology, 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. Glaucius Oliva
2. L. Ferreira
3. Ricardo N Dos Santos
4. Jonathan Romero
5. Alán Aspuru-Guzik
6. Sun Choi
7. Sandeepkumar Kothiwale
8. Maria Batool

In Organic Chemistry

1. Jonathan Romero
2. Alán Aspuru-Guzik
3. Sun Choi
4. Glaucius Oliva
5. Sandeepkumar Kothiwale
6. Maria Batool
7. Jens Meiler
8. E. W. Lowe

In Pharmacology

1. Alán Aspuru-Guzik
2. Sandeepkumar Kothiwale
3. Maria Batool
4. Jens Meiler
5. E. W. Lowe
6. Adriano Defini Andricopulo
7. Sangdun Choi
8. Gregory Sliwoski

In Computational Chemistry & Drug Discovery

1. Alán Aspuru-Guzik
2. Sun Choi
3. Glaucius Oliva
4. Sandeepkumar Kothiwale
5. Maria Batool
6. Jens Meiler
7. E. W. Lowe
8. Vijayakumar Gosu

Citations in the corpus

• Molecular Docking and Structure-Based Drug Design Strategies (2015, background): “…structure-based virtual screening (SBVS) and molecular dynamics (MD) are among the most frequently used SBDD strategies due to their wide range of applications in the analysis of molecular recognition events such as binding energetics, molecular interactions and induced conformational changes [6].”
• Role of computer-aided drug design in modern drug discovery (2015, background): “…from structural and computational biology aided in the generation of protein structures with the use of X-ray crystallography, nuclear magnetic resonance (NMR), cryo-electron microscopy (EM), homology modeling, and molecular dynamic (MD) simulations (Anderson 2003; Kalyaanamoorthy and Chen 2011).”
• Computational Methods in Drug Discovery (2014, background): “Structure-based CADD relies on the knowledge of the target protein structure to calculate interaction energies for all compounds tested, whereas ligand-based CADD exploits the knowledge of known active and inactive molecules through chemical similarity searches or construction of predictive, quantit”
• Potential of quantum computing for drug discovery (2018, background): “A crucial aspect of target characterization is the recognition of binding sites, which are those regions of the protein where the drug candidate is more likely to bind and effect the desirable therapeutic effect [16].”
• Structure-based drug design with equivariant diffusion models (2022, unknown): “To further demonstrate the limitations of the coarse-grained models, we compare the generated raw conformations to the best scoring QuickVina docking pose after re-docking and plot the distribution Table F6 : Evaluation of generated molecules for targets from the CrossDocked and Binding MOAD test…”
• A Structure-Based Drug Discovery Paradigm (2019, methodology): “These methods have numerous applications in the analysis of binding energetics, ligand–protein interactions, and evaluation of the conformational changes occurring during the docking process [10].”
• In silico methods and tools for drug discovery (2021, background): “A binding site is a concave region or a small pocket of a protein where a ligand molecule binds to produce the desired output (activation, inhibition, or modulation) [11,147].”