AIMC Topic: Drug Combinations

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A review of machine learning approaches for drug synergy prediction in cancer.

Briefings in bioinformatics May 13, 2022
Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment strategy for complex diseases such as malignancies. Identifying synergistic combinations with various compounds and structures requires testing a large number ...
Machine Learning Drug Synergism Neoplasms Humans Computational Biology Drug Combinations
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SynPathy: Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning.

Molecular cancer research : MCR May 4, 2022
UNLABELLED: Drug combination therapy has become a promising therapeutic strategy for cancer treatment. While high-throughput drug combination screening is effective for identifying synergistic drug combinations, measuring all possible combinations is...
Humans Deep Learning Protein Interaction Maps Drug Combinations Drug Synergism Computational Biology Drug Therapy, Combination
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An enhanced cascade-based deep forest model for drug combination prediction.

Briefings in bioinformatics Mar 10, 2022
Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artif...
Artificial Intelligence Machine Learning Computational Biology Neural Networks, Computer Drug Combinations
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PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.

Briefings in bioinformatics Mar 10, 2022
Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds i...
Drug Combinations Cell Line Machine Learning Neural Networks, Computer Protein Interaction Maps
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DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations.

Briefings in bioinformatics Jan 17, 2022
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 hard to screen synergistic drug combinations through wet-lab experime...
Drug Combinations Humans Neural Networks, Computer Software Neoplasms Machine Learning
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Machine learning methods, databases and tools for drug combination prediction.

Briefings in bioinformatics Jan 17, 2022
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, experimental methods are insufficient to explore novel drug combinations. In ...
Algorithms Databases, Factual Drug Combinations Machine Learning Drug Interactions
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Effectiveness and safety of elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil fumarate single-tablet combination among HIV-infected patients in Turkey: results from a real world setting.

African health sciences Dec 1, 2021
BACKGROUND: Efficacy of elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil (E/C/F/TDF) in treatment-naïve and experienced patients with HIV infection was demonstrated in phase 3 trials. The primary objective of this study was to evaluate effe...
Homosexuality, Male Quinolones Turkey HIV Infections Cobicistat Adult Tenofovir Anti-HIV Agents Emtricitabine Drug Combinations Male Humans Tablets Retrospective Studies
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Comparative analysis of molecular fingerprints in prediction of drug combination effects.

Briefings in bioinformatics Nov 5, 2021
Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computa...
Datasets as Topic Uncertainty High-Throughput Screening Assays Drug Synergism Deep Learning Humans Cell Line, Tumor Computer Simulation Drug Combinations Regression Analysis Drug Discovery Drug Interactions
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GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction.

Journal of the American Medical Informatics Association : JAMIA Oct 12, 2021
OBJECTIVE: To develop an end-to-end deep learning framework based on a protein-protein interaction (PPI) network to make synergistic anticancer drug combination predictions.
Algorithms Deep Learning Neural Networks, Computer Antineoplastic Combined Chemotherapy Protocols Drug Combinations
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Deep learning identifies synergistic drug combinations for treating COVID-19.

Proceedings of the National Academy of Sciences of the United States of America Sep 28, 2021
Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role i...
Alanine Adenosine Monophosphate COVID-19 Drug Treatment Drug Synergism Cell Survival Drug Interactions Deep Learning Antiviral Agents Drug Combinations Humans SARS-CoV-2
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