Guided docking as a data generation approach facilitates structure-based machine learning on kinases

22 December 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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 (GNN). 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 DTI models only.

Keywords

data-driven drug discovery
structure-based machine learning
E(3)-invariant graph neural networks
template docking
kinases

Supplementary weblinks

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