Efficient virtual high-content screening using a distance-aware transformer model.

ABSTRACT: Molecular similarity search is an often-used method in drug discovery, especially in virtual screening studies. While simple one- or two-dimensional similarity metrics can be applied to search databases containing billions of molecules in a reasonable amount of time, this is not the case for complex three-dimensional methods. In this work, we trained a transformer model to autoencode tokenized SMILES strings using a custom loss function developed to conserve similarities in latent space. This allows the direct sampling of molecules in the generated latent space based on their Euclidian distance. Reducing the similarity between molecules to their Euclidian distance in latent space allows the model to perform independent of the similarity metric it was trained on. While we test the method he

SUBMITTER: Sellner MS 

PROVIDER: S-EPMC9906956 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature