[HTML][HTML] On the road to explainable AI in drug-drug interactions prediction: A systematic review
Over the past decade, polypharmacy instances have been common in multi-diseases
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …
Computational/in silico methods in drug target and lead prediction
Drug-like compounds are most of the time denied approval and use owing to the
unexpected clinical side effects and cross-reactivity observed during clinical trials. These …
unexpected clinical side effects and cross-reactivity observed during clinical trials. These …
Modeling polypharmacy side effects with graph convolutional networks
Motivation The use of drug combinations, termed polypharmacy, is common to treat patients
with complex diseases or co-existing conditions. However, a major consequence of …
with complex diseases or co-existing conditions. However, a major consequence of …
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 …
Combinatorial drug therapy for cancer in the post-genomic era
Over the past decade, whole genome sequencing and other'omics' technologies have
defined pathogenic driver mutations to which tumor cells are addicted. Such addictions …
defined pathogenic driver mutations to which tumor cells are addicted. Such addictions …
Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review
Despite considerable progress in genome-and proteome-based high-throughput screening
methods and in rational drug design, the increase in approved drugs in the past decade did …
methods and in rational drug design, the increase in approved drugs in the past decade did …
Systems pharmacology for investigation of the mechanisms of action of traditional Chinese medicine in drug discovery
W Zhang, Y Huai, Z Miao, A Qian… - Frontiers in pharmacology, 2019 - frontiersin.org
As a traditional medical intervention in Asia and a complementary and alternative medicine
in western countries, traditional Chinese medicine (TCM) has attracted global attention in …
in western countries, traditional Chinese medicine (TCM) has attracted global attention in …
Drug–drug interaction prediction: databases, web servers and computational models
In clinical treatment, two or more drugs (ie drug combination) are simultaneously or
successively used for therapy with the purpose of primarily enhancing the therapeutic …
successively used for therapy with the purpose of primarily enhancing the therapeutic …
From single drug targets to synergistic network pharmacology in ischemic stroke
Drug discovery faces an efficacy crisis to which ineffective mainly single-target and symptom-
based rather than mechanistic approaches have contributed. We here explore a mechanism …
based rather than mechanistic approaches have contributed. We here explore a mechanism …
NLLSS: predicting synergistic drug combinations based on semi-supervised learning
Fungal infection has become one of the leading causes of hospital-acquired infections with
high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases …
high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases …