Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-
consuming, and challenging task. How to reduce the research costs and speed up the …

Highlights•Basic principles of commonly used deep learning architectures.•Drug–target
interactions based deep learning approaches for drug repositioning.•Heterogeneous …

Every day, enormous amounts of biomedical texts discussing various biomedical topics are
produced. Revealing strong semantic connections hidden in those unstructured data is …

Drug repositioning, which involves the identification of new therapeutic indications for
approved drugs, considerably reduces the time and cost of develo** new drugs. Recent …

Accurately identifying new therapeutic uses for drugs is crucial for advancing
pharmaceutical research and development. Matrix factorization is often used in association …

Network biology finds application in interpreting molecular interaction networks and
providing insightful inferences using graph theoretical analysis of biological systems. The …

Drug combination therapies are successfully used in the treatment of cancer disease. The
synergistic drug combinations not only increase the drug efficacy, but also reduce the drug …

Exploring efficient and high-accuracy computational drug repositioning methods has
become a popular and attractive topic in drug development. This technology can …

Similarity network fusion (SNF) is increasingly employed for multi-omics and microbiome
data integration and assists patient endoty**. This Methods article describes its …