Topology-Based and Conformation-Based Decoys Database: An Unbiased Online Database for Training and Benchmarking Machine-Learning Scoring Functions.

Abstract

Machine-learning-based scoring functions (MLSFs) have gained attention for their potential to improve accuracy in binding affinity prediction and structure-based virtual screening (SBVS) compared to classical SFs. Developing accurate MLSFs for SBVS requires a large and unbiased dataset that includes structurally diverse actives and decoys. Unfortunately, most datasets suffer from hidden biases and data insufficiency. Here, we developed topology-based and conformation-based decoys database (ToCoDDB). The biological targets and active ligands in ToCoDDB were collected from scientific literature and established datasets. The decoys were generated and debiased by using conditional recurrent neural networks and molecular docking. ToCoDDB is presently the largest unbiased database with 2.4 million decoys encompassing 155 targets. The detailed information and performance benchmark for each target are provided, which are beneficial for training and evaluating MLSFs. Moreover, the online decoys generation function of ToCoDDB further expands its application range to any target. ToCoDDB is freely available at http://cadd.zju.edu.cn/tocodecoy/.