Synergy and antagonism in natural product extracts: when 1+ 1 does not equal 2
Covering: 2000 to 2019 According to a 2012 survey from the Centers for Disease Control
and Prevention, approximately 18% of the US population uses natural products (including …
and Prevention, approximately 18% of the US population uses natural products (including …
Machine learning approaches to drug response prediction: challenges and recent progress
Cancer is a leading cause of death worldwide. Identifying the best treatment using
computational models to personalize drug response prediction holds great promise to …
computational models to personalize drug response prediction holds great promise to …
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases
The identification of interactions between drugs/compounds and their targets is crucial for
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …
Application of computational biology and artificial intelligence in drug design
Traditional drug design requires a great amount of research time and developmental
expense. Booming computational approaches, including computational biology, computer …
expense. Booming computational approaches, including computational biology, computer …
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
Motivation While drug combination therapies are a well-established concept in cancer
treatment, identifying novel synergistic combinations is challenging due to the size of …
treatment, identifying novel synergistic combinations is challenging due to the size of …
Machine learning for drug-target interaction prediction
Identifying drug-target interactions will greatly narrow down the scope of search of candidate
medications, and thus can serve as the vital first step in drug discovery. Considering that in …
medications, and thus can serve as the vital first step in drug discovery. Considering that in …
DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy
Drug combinations have demonstrated high efficacy and low adverse side effects compared
to single drug administration in cancer therapies and thus have drawn intensive attention …
to single drug administration in cancer therapies and thus have drawn intensive attention …
Machine learning methods, databases and tools for drug combination prediction
L Wu, Y Wen, D Leng, Q Zhang, C Dai… - Briefings in …, 2022 - academic.oup.com
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 …
reduce the development of drug resistance. However, even with high-throughput screens …
[HTML][HTML] Machine learning in the prediction of cancer therapy
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 …
cancer-related deaths. Resistance can occur at any time during the treatment, even at the …
Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs
Combination therapy is a fundamental strategy in cancer chemotherapy. It involves
administering two or more anti-cancer agents to increase efficacy and overcome multidrug …
administering two or more anti-cancer agents to increase efficacy and overcome multidrug …