AIMC Topic: Drug Combinations

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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...
Deep Learning Cell Line, Tumor Drug Combinations Humans Computer Simulation Drug Discovery Drug Synergism Datasets as Topic Uncertainty Regression Analysis Drug Interactions High-Throughput Screening Assays
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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.
Neural Networks, Computer Algorithms Deep Learning 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...
SARS-CoV-2 Deep Learning Antiviral Agents Humans Drug Combinations COVID-19 Drug Treatment Adenosine Monophosphate Cell Survival Drug Synergism Drug Interactions Alanine
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[Exploration on rationality evaluation approach of drug combination medication based on sequential analysis and machine learning].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica May 1, 2021
Drug combination is a common clinical phenomenon. However, the scientific implementation of drug combination is li-mited by the weak rational evaluation that reflects its clinical characteristics. In order to break through the limitations of existing...
Artificial Intelligence Machine Learning Technology Drug Combinations
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Anticancer drug synergy prediction in understudied tissues using transfer learning.

Journal of the American Medical Informatics Association : JAMIA Jan 15, 2021
OBJECTIVE: Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies...
Drug Screening Assays, Antitumor Drug Synergism Machine Learning Antineoplastic Agents Cell Line, Tumor Humans Computational Biology Neural Networks, Computer Models, Theoretical Drug Combinations
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Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
Intrinsic and acquired drug resistance is a major challenge in cancer therapy. Synergistic drug combinations could help to overcome drug resistance. However, the number of possible drug combinations is enormous, and it is infeasible to experimentally...
Drug Synergism Antineoplastic Combined Chemotherapy Protocols Drug Combinations Databases, Genetic Computer Simulation Humans Neoplasms Neural Networks, Computer Gene Expression Profiling Databases, Factual Deep Learning Cell Line, Tumor Computational Biology
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Application of machine intelligence technology in the detection of vaccines and medicines for SARS-CoV-2.

European review for medical and pharmacological sciences Nov 1, 2020
Researchers have found many similarities between the 2003 severe acute respiratory syndrome (SARS) virus and SARS-CoV-19 through existing data that reveal the SARS's cause. Artificial intelligence (AI) learning models can be created to predict drug s...
Vitamins Ascorbic Acid Ritonavir COVID-19 Drug Treatment Ribavirin COVID-19 Adenosine Monophosphate Drug Combinations Alanine Deep Learning Immunosuppressive Agents Lopinavir Antiviral Agents Drug Repositioning COVID-19 Vaccines Chloroquine Humans Drug Development Hydroxychloroquine Artificial Intelligence Antibodies, Monoclonal, Humanized Machine Learning
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[Synergistic drug combination prediction in multi-input neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Aug 25, 2020
Synergistic effects of drug combinations are very important in improving drug efficacy or reducing drug toxicity. However, due to the complex mechanism of action between drugs, it is expensive to screen new drug combinations through trials. It is wel...
Antineoplastic Agents Computational Biology Drug Combinations Humans Neural Networks, Computer Neoplasms
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Network-principled deep generative models for designing drug combinations as graph sets.

Bioinformatics (Oxford, England) Jul 1, 2020
MOTIVATION: Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous ch...
Neural Networks, Computer Drug Discovery Drug Combinations
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Effects of Midazolam and Midazolam-Butorphanol on Gastrointestinal Transit Time and Motility in Cockatiels ().

Journal of avian medicine and surgery Dec 1, 2018
Positive contrast gastrointestinal (GI) studies are performed frequently in avian medicine to identify GI obstruction, luminal distension, and intracoelomic mass effects. However, repeated manual restraint and radiographic positioning may result in a...
Cockatoos Animals Hypnotics and Sedatives Gastrointestinal Transit Drug Combinations Random Allocation Midazolam Butorphanol
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