Simple Summary The rapidly advancing field of deep learning, specifically transformer-
based architectures and attention mechanisms, has found substantial applicability in …

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many
cancer-related deaths. Resistance can occur at any time during the treatment, even at the …

Motivation Screening bioactive compounds in cancer cell lines receive more attention.
Multidisciplinary drugs or drug combinations have a more effective role in treatments and …

Large language models (LLMs) have been shown to have significant potential in few-shot
learning across various fields, even with minimal training data. However, their ability to …

Drug–drug interaction (DDI) prediction identifies interactions of drug combinations in which
the adverse side effects caused by the physicochemical incompatibility have attracted much …

Motivation Drug combination therapy has become an increasingly promising method in the
treatment of cancer. However, the number of possible drug combinations is so huge that it is …

Combinatorial therapies that target multiple pathways have shown great promises for
treating complex diseases. DrugComb (https://drugcomb. org/) is a web-based portal for the …

Identifying synergistic drug combinations is fundamentally important to treat a variety of
complex diseases while avoiding severe adverse drug–drug interactions. Although several …

As the global burden of antibiotic resistance continues to grow, creative approaches to
antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly …

Motivation Drug combinations have exhibited promise in treating cancers with less toxicity
and fewer adverse reactions. However, in vitro screening of synergistic drug combinations is …