Topology-based
and Conformation-
based Decoys Database
An unbiased database for the training and benchmarking of machine-learning scoring functions, providing not only 155 target-specific datasets but also a decoys generation interface.
datasets for machine-learning scoring functions.
Machine-learning-based scoring functions (MLSFs) have attracted extensive attention due to their potentially improved accuracy in binding affinity prediction and/or structure-based virtual screening (SBVS) compared with classical scoring functions (SFs). Development of accurate MLSFs for SBVS against a given target requires a large unbiased dataset with structurally diverse actives and decoys. However, most existing datasets for the development of MLSFs were originally designed for traditional SFs and may suffer from hidden biases (artificial enrichment, analogue bias, domain bias and noncausal bias) and data insufficiency. Hereby, we developed a new database named Topology-based and Conformation-based decoys database (ToCoDDB), which can not only provide 155 target-specific unbiased datasets but also can generate unbiased and expandable datasets for training and benchmarking MLSFs.
overview of the database
Super-family
Targets
Actives
Decoys
You can contact us through email
tingjunhou@zju.edu.cn
Zhejiang University
Hangzhou, Zhejiang, China
oriental-cds@163.com
Central South University
Changsha, Hunan, China
xujunzhang@zju.edu.cn
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