Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of develo** new drugs. Deep learning (DL) …

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

Combination therapies are superior to monotherapy for many cancers. This advantage was
historically ascribed to the ability of combinations to address tumor heterogeneity, but …

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

In recent years, with the increase of computer computing power, Deep Learning has begun
to be favored. Its learning of non-linear feature combinations has played a role that …

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 …

Combination therapy has shown an obvious efficacy on complex diseases and can greatly
reduce the development of drug resistance. However, even with high-throughput screens …

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning
(ML) are under increased pressure to develop algorithms faster than ever. The possibility of …

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