  Drug repositioning is an economically efficient strategy used to discover new
indications for existing drugs beyond their original approvals, expanding their
applicability and usage to address challenges in disease treatment. In recent
years, deep-learning techniques for drug repositioning have gained much
attention. While most deep learning-based research methods focus on encoding
drugs and diseases by extracting feature information from neighbors in the
network, they often pay little attention to the potential relationships between
the features of drugs and diseases, leading to imprecise encoding of drugs and
diseases. To address this, we design a dual-feature drug repositioning neural
network (DFDRNN) model to achieve precise encoding of drugs and diseases.
DFDRNN uses two features to represent drugs and diseases: the similarity
feature and the association feature. The model incorporates a self-attention
mechanism to design two dual-feature extraction modules for achieving precisely
encoding of drugs and diseases: the intra-domain dual-feature extraction
(IntraDDFE) module and the inter-domain dual-feature extraction (InterDDFE)
module. The IntraDDFE module extracts features from a single domain (drug or
disease domain), while the InterDDFE module extracts features from the mixed
domain (drug and disease domain). In particular, the feature is changed by
InterDDFE, ensuring a precise encoding of drugs and diseases. Finally, a
cross-dual-domain decoder is designed to predict drug-disease associations in
both the drug and disease domains. Compared to six state-of-the-art methods,
DFDRNN outperforms others on four benchmark datasets, with an average AUROC of
0.946 and an average AUPR of 0.597.
