Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction Prediction
Journal:
arXiv
Published Date:
Jan 26, 2025
Abstract
Significant differences in protein structures hinder the generalization of
existing drug-target interaction (DTI) models, which often rely heavily on
pre-learned binding principles or detailed annotations. In contrast, BioBridge
designs an Inductive-Associative pipeline inspired by the workflow of
scientists who base their accumulated expertise on drawing insights into novel
drug-target pairs from weakly related references. BioBridge predicts novel
drug-target interactions using limited sequence data, incorporating multi-level
encoders with adversarial training to accumulate transferable binding
principles. On these principles basis, BioBridge employs a dynamic prototype
meta-learning framework to associate insights from weakly related annotations,
enabling robust predictions for previously unseen drug-target pairs. Extensive
experiments demonstrate that BioBridge surpasses existing models, especially
for unseen proteins. Notably, when only homologous protein binding data is
available, BioBridge proves effective for virtual screening of the epidermal
growth factor receptor and adenosine receptor, underscoring its potential in
drug discovery.
