Comparative Assessment of Scoring Functions: The CASF-2016 Update

By Minyi Su, Qifan Yang, Yu Du, Guoqin Feng, Zhihai Liu, Yan Li, Renxiao Wang. 2018

698 citations recorded by the source corpus.

In structure-based drug design, scoring functions are often employed to evaluate protein-ligand interactions. A variety of scoring functions have been developed so far, and thus, some objective benchmarks are desired for assessing their strength and weakness. The comparative assessment of scoring functions (CASF) benchmark developed by us provides an answer to this demand. CASF is designed as a "scoring benchmark", where the scoring process is decoupled from the docking process to depict the performance of scoring function more precisely. Here, we describe the latest update of this benchmark, i.e., CASF-2016. Each scoring function is still evaluated by four metrics, including "scoring power", "ranking power", "docking power", and "screening power". Nevertheless, the evaluation methods have been improved considerably in several aspects. A new test set is compiled, which consists of 285 protein-ligand complexes with high-quality crystal structures and reliable binding constants. A panel of 25 scoring functions are tested on CASF-2016 as a demonstration. Our results reveal that the performance of current scoring functions is more promising in terms of docking power than scoring, ranking, and screening power. Scoring power is somewhat correlated with ranking power, so are docking power and screening power. The results obtained on CASF-2016 may provide valuable guidance for the end users to make smart choices among available scoring functions. Moreover, CASF is created as an open-access benchmark so that other researchers can utilize it to test a wider range of scoring functions. The complete CASF-2016 benchmark will be released on the PDBbind-CN web server ( http://www.pdbbind-cn.org/casf.asp/ ) once this article is published.

Subfields: Computational Theory and Mathematics, Molecular Biology

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

In Molecular Biology

Citations in the corpus

Evaluation of AlphaFold2 structures as docking targets (2022, methodology): “We are adopting the structure and standards of the CASF competition (Su et al., 2019), in which comparisons between docking methodologies are usually made, to instead evaluate the source of the target structure.”
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity (2021, background): “Moreover, the core set with 290 complexes serves as the highest quality benchmark for testing through a careful selection process [36].”
Equivariant 3D-conditional diffusion model for molecular linker design (2022, neutral): “[23] is taken from the CASF-2016 dataset [49].”
Uni-Mol: A Universal 3D Molecular Representation Learning Framework (2023, methodology): “For the benchmark dataset, referring to the previous works [28, 63], we use CASF-2016 Two benchmarks are conducted: 1) Docking power, the default metric to benchmark the ability of a scoring function in CASF-2016.”