Abstract
Molecular representation learning (MRL) is a specialized field in which deep-learning models condense essential molecular information into a vectorized form. Whereas recent research has predominantly emphasized drug discovery and bioactivity applications, MRL holds significant potential for diverse chemical properties beyond these contexts. The recently published study by King-Smith introduces a novel application of molecular representation training and compellingly demonstrates its value in predicting molecular properties (E. King-Smith, Chem. Sci., 2024, https://doi.org/10.1039/D3SC04928K). In this focus article, we will briefly delve into MRL in chemistry and the significance of King-Smith's work within the dynamic landscape of this evolving field.
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