Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases.

Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand complex structures. Exemplified for kinase drug discovery, we address this issue by generating kinase-ligand complex data using template docking for the kinase compound subset of available ChEMBL assay data. To evaluate the benefit of the created complex data, we use it to train a structure-based E(3)-invariant graph neural network. Our evaluation shows that binding affinities can be predicted with significantly higher precision by models that take synthetic binding poses into account compared to ligand- or drug-target interaction models alone.

Hit optimization by dynamic combinatorial chemistry on Streptococcus pneumoniae energy-coupling factor transporter ECF-PanT.

Herein, we present the first application of target-directed dynamic combinatorial chemistry (tdDCC) to the whole complex of the highly dynamic transmembrane, energy-coupling factor (ECF) transporter ECF-PanT in Streptococcus pneumoniae. In addition, we successfully employed the tdDCC technique as a hit-identification and -optimization strategy that led to the identification of optimized ECF inhibitors with improved activity. We characterized the best compounds regarding cytotoxicity and performed computational modeling studies on the crystal structure of ECF-PanT to rationalize their binding mode. Notably, docking studies showed that the acylhydrazone linker is able to maintain the crucial interactions.