Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study

In the context of drug discovery, computational methods were able to accelerate the challenging process of designing and optimizing a new drug candidate. Amongst the possible atomistic simulation approaches, metadynamics (metaD) has proven very powerful. However, the choice of collective variables (CVs) is not trivial for complex systems. To automate the process of CVs identification, two different machine learning algorithms were applied in this study, namely DeepLDA and Autoencoder, to the metaD simulation of a well-researched drug/target complex, consisting in a pharmacologically relevant non-canonical DNA secondary structure (G-quadruplex) and a metallodrug acting as its stabilizer, as well as solvent molecules. Machine Learning-powered metadynamics enabling the unbiased selection of collective variables to describe the binding free energy of a non-covalent metallodrug/DNA G-quadruplex adduct and the effects of water solvation.+image

Automated summary

In the context of drug discovery, computational methods have been utilized to accelerate the design and optimization of new drug candidates. This study applied machine learning algorithms to automatically identify the collective variables that describe the binding free energy and solvation effects of a drug and DNA complex.