Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space.

ABSTRACT: Given the prolonged timelines and high costs associated with traditional approaches, accelerating drug development is crucial. Computational methods, particularly drug-target interaction prediction, have emerged as efficient tools, yet the explainability of machine learning models remains a challenge. Our work aims to provide more interpretable interaction prediction models using similarity-based prediction in a latent space aligned to biological hierarchies. We investigated integrating drug and protein hierarchies into a joint-embedding drug-target latent space via embedding regularization by conducting a comparative analysis between models employing traditional flat Euclidean vector spaces and those utilizing hyperbolic embeddings. Besides, we provided a latent space analysis as an examp

SUBMITTER: Pogany D 

PROVIDER: S-EPMC10956837 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature