A comparison of embedding aggregation strategies in drug-target interaction prediction.

ABSTRACT: The prediction of interactions between novel drugs and biological targets is a vital step in the early stage of the drug discovery pipeline. Many deep learning approaches have been proposed over the last decade, with a substantial fraction of them sharing the same underlying two-branch architecture. Their distinction is limited to the use of different types of feature representations and branches (multi-layer perceptrons, convolutional neural networks, graph neural networks and transformers). In contrast, the strategy used to combine the outputs (embeddings) of the branches has remained mostly the same. The same general architecture has also been used extensively in the area of recommender systems, where the choice of an aggregation strategy is still an open question. In this work, we inve

SUBMITTER: Iliadis D 

PROVIDER: S-EPMC10845509 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature