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

Journal of Cheminformatics

Research

Manuel S. Sellner¹  Amr H. Mahmoud¹  Markus A. Lill¹ 

[1] Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland;

关键词: Virtual screening;    Similarity search;    Deep learning;    Transformer model;   

DOI  :  10.1186/s13321-023-00686-z

 received in 2022-06-19, accepted in 2023-01-22,  发布年份 2023

来源: Springer

【 摘 要 】

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 here using 2D similarity as proof-of-concept study, the algorithm will enable also high-content screening with time-consuming 3D similarity metrics. We show that the presence of a specific loss function for similarity conservation greatly improved the model’s ability to predict highly similar molecules. When applying the model to a database containing 1.5 billion molecules, our model managed to reduce the relevant search space by 5 orders of magnitude. We also show that our model was able to generalize adequately when trained on a relatively small dataset of representative structures. The herein presented method thereby provides new means of substantially reducing the relevant search space in virtual screening approaches, thus highly increasing their throughput. Additionally, the distance awareness of the model causes the efficiency of this method to be independent of the underlying similarity metric.

【 授权许可】

CC BY   
© The Author(s) 2023

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