Consensus scoring for protein-ligand interactions.

By Miklos Feher. 2006

232 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, 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. Hong-Xing Zhang
5. Sandeepkumar Kothiwale
6. Jens Meiler
7. E. W. Lowe
8. Meng Cui

In Materials Chemistry

1. Glaucius Oliva
2. Hong-Xing Zhang
3. Sandeepkumar Kothiwale
4. Jens Meiler
5. E. W. Lowe
6. Meng Cui
7. Mihaly Mezei
8. Adriano Defini Andricopulo

In Molecular Biology

1. Glaucius Oliva
2. L. Ferreira
3. Ricardo N Dos Santos
4. Hong-Xing Zhang
5. Sandeepkumar Kothiwale
6. Jens Meiler
7. E. W. Lowe
8. Meng Cui

In Computational Chemistry & Drug Discovery

1. Glaucius Oliva
2. Hong-Xing Zhang
3. Sandeepkumar Kothiwale
4. Jens Meiler
5. E. W. Lowe
6. Meng Cui
7. Mihaly Mezei
8. Adriano Defini Andricopulo

Citations in the corpus

• Molecular Docking and Structure-Based Drug Design Strategies (2015, background): “This can be very useful, as it combines the advantages and simultaneously attenuates the shortcomings of each method [64].”
• Virtual screening strategies in drug discovery: a critical review. (2013, background): “Strategies proposed to improve the performance of current scoring functions include adding other factors to account for solvation and entropic effects [80] deriving more accurate energy terms by means of high-level quantum calculations [84], target-specific scoring functions [85] and consensus scori”
• Computational Methods in Drug Discovery (2014, background): “These results can then be combined in different ways to rank solutions (Feher, 2006).”
• Molecular docking: a powerful approach for structure-based drug discovery. (2011, background): “Also, the usefulness of consensus scoring diminishes when terms in different scoring functions are significantly correlated [5, 93].”