Comparative Assessment of Scoring Functions on a Diverse Test Set

By Tiejun Cheng, Xun Li, Yan Li, Zhihai Liu, Renxiao Wang. Journal of Chemical Information and Modeling, 2009

498 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1b4ad654190dcb7135b5aef4ecc8b67577d0d97a

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. Stephen H. Bryant
2. Jianyang Zeng
3. Fangping Wan
4. Tatu Pantsar
5. G. Wei
6. Sun Choi
7. Elizabeth Yuriev
8. Chee Keong Kwoh

In Molecular Biology

1. Stephen H. Bryant
2. Jianyang Zeng
3. Fangping Wan
4. Yanli Wang
5. Tiejun Cheng
6. Tatu Pantsar
7. G. Wei
8. Sun Choi

Citations in the corpus

• Binding Affinity via Docking: Fact and Fiction (2018, methodology): “[16], in which the frequently used CASF-2007 data set was employed to evaluate docking performance.”
• Role of computer-aided drug design in modern drug discovery (2015, background): “Consensus scoring has superior accuracy, thereby improving the probability of discovering true potential hits (Cheng et al. 2009; Houston and Walkinshaw 2013).”
• Fast, accurate, and reliable molecular docking with QuickVina 2 (2015, background): “The local search is the most time-consuming part of the optimization.”
• Challenges and advances in computational docking: 2009 in review (2011, methodology): “It is widely accepted that meaningful comparison of docking approaches (algorithms and/ or scoring functions) is difficult, non-trivial (182), and sometimes ‘‘intriguing’’ (85).”
• A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking (2010, unknown): “A number of studies (e.g. Cheng et al., 2009; Ferrara et al., 2004; Wang et al., 2003; Wang et al., 2004) have validated scoring functions based on their ability to predict the binding affinities of diverse protein–ligand complexes.”
• Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review (2012, background): “Despite the relatively coarse de fi nition of ligand – target atom pairs, which considers only atomic number with no concern about distance dependence, RF-score strikingly out-performed all 16 state-of-the-art scoring functions in a recent benchmark (73).”