Predicting protein-ligand bioactivities is known to be challenging yet crucial in any drug discovery project. In a protein structure-based scenario, supervised machine-learning models have been highly competitive for at least 30 years. Regardless of the machine-learning method used, data set size and quality are key aspects in model training and validation. In general, data sets are the foundation upon which accurate performance estimates can be obtained. While well-curated repositories exist for bioactivity and protein structure data, combining these two types of data is particularly challenging. With ActivityFinder, we recently introduced a fully automated process for linking these data sources relying on protein sequence and molecular structure only. By combining ActivityFinder with previously developed tools for structure quality estimation and property calculation, we created StrAcTable, an automatically constructed data set of annotated protein-ligand complexes. The automated procedure allows for continued and sustainable growth. StrAcTable includes detailed descriptions of the quality of matching between ChEMBL and PDB, of the macromolecular structure, small-molecule ligands bound, and bioactivity data from ChEMBL. Based on ChEMBL Version 35, the StrAcTable contains 20,063 protein-ligand complexes with bioactivity values, enabling an efficient construction of training and validation data sets for structure-based molecular design method development. With that, StrAcTable aims to be the basis for novel data sets that can be used to train and test ML and traditional methods.
Gutermuth et al. (Fri,) studied this question.