Applied machine learning for predicting the lanthanide-ligand binding affinities.

ABSTRACT: Binding affinities of metal-ligand complexes are central to a multitude of applications like drug design, chelation therapy, designing reagents for solvent extraction etc. While state-of-the-art molecular modelling approaches are usually employed to gather structural and chemical insights about the metal complexation with ligands, their computational cost and the limited ability to predict metal-ligand stability constants with reasonable accuracy, renders them impractical to screen large chemical spaces. In this context, leveraging vast amounts of experimental data to learn the metal-binding affinities of ligands becomes a promising alternative. Here, we develop a machine learning framework for predicting binding affinities (logK₁) of lanthanide cations with several structurally

SUBMITTER: Chaube S 

PROVIDER: S-EPMC7459320 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature