Application of automated machine learning in the identification of multi-target-directed ligands blocking PDE4B, PDE8A, and TRPA1 with potential use in the treatment of asthma and COPD

Asthma and COPD are characterized by complex pathophysiology associated with chronic inflammation, bronchoconstriction, and bronchial hyperresponsiveness resulting in airway remodeling. A possible comprehensive solution that could fully counteract the pathological processes of both diseases are rationally designed multi-target-directed ligands (MTDLs), combining PDE4B and PDE8A inhibition with TRPA1 blockade. The aim of the study was to develop AutoML models to search for novel MTDL chemotypes blocking PDE4B, PDE8A, and TRPA1. Regression models were developed for each of the biological targets using mljar-supervised. On their basis, virtual screenings of commercially available compounds derived from the ZINC15 database were performed. A common group of compounds placed within the top results was selected as potential novel chemotypes of multifunctional ligands. This study represents the first attempt to discover the potential MTDLs inhibiting three biological targets. The obtained results prove the usefulness of AutoML methodology in the identification of hits from the big compound databases.

Automated summary

Asthma and COPD are complex diseases involving chronic inflammation, bronchoconstriction, and airway remodeling. A potential solution to target these pathological processes is the development of multi-target-directed ligands (MTDLs) that inhibit PDE4B, PDE8A, and TRPA1. In this study, AutoML models were used to search for novel MTDL chemotypes blocking these biological targets. The results demonstrate the usefulness of AutoML methodology in identifying potential hits from large compound databases.