AIMC Topic: Drug Synergism
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Joint Toxicity of Lead, Chromium, Cobalt and Nickel to Photobacterium phosphoreum at No Observed Effect Concentration.
Bulletin of environmental contamination and toxicology
Jun 2, 2015
Joint toxicity of Pb2+, Cr3+, Co2+ and Ni2+ toward Photobacterium phosphoreum (Ph. phosphoreum) at the no observed effect concentration (NOEC) was determined through a factorial experiment. A neural network model was designed according to experimenta...
Accurate prediction of synergistic drug combination using a multi-source information fusion framework.
BMC biology
Jul 3, 2025
BACKGROUND: Accurately predicting synergistic drug combinations is critical for complex disease therapy. However, the vast search space of potential drug combinations poses significant challenges for identification through biological experiments alon...
PathSynergy: a deep learning model for predicting drug synergy in liver cancer.
Briefings in bioinformatics
Mar 4, 2025
Cancer is a major public health problem while liver cancer is the main cause of global cancer-related deaths. The previous study demonstrates that the 5-year survival rate for advanced liver cancer is only 30%. Few of the first-line targeted drugs in...
Scaling up drug combination surface prediction.
Briefings in bioinformatics
Mar 4, 2025
Drug combinations are required to treat advanced cancers and other complex diseases. Compared with monotherapy, combination treatments can enhance efficacy and reduce toxicity by lowering the doses of single drugs-and there especially synergistic com...
DD-PRiSM: a deep learning framework for decomposition and prediction of synergistic drug combinations.
Briefings in bioinformatics
Nov 22, 2024
Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex i...
Predicting drug synergy using a network propagation inspired machine learning framework.
Briefings in functional genomics
Jul 19, 2024
Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number o...
Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction.
Current medicinal chemistry
Jan 1, 2024
Currently, the main therapeutic methods for cancer include surgery, radiation therapy, and chemotherapy. However, chemotherapy still plays an important role in tumor therapy. Due to the variety of pathogenic factors, the development process of tumors...
CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.
Briefings in bioinformatics
Jan 19, 2023
Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug sy...
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 ...
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...
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