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Drug Synergism

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Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction.

Current medicinal chemistry
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...

Combination therapy synergism prediction for virus treatment using machine learning models.

PloS one
Combining different drugs synergistically is an essential aspect of developing effective treatments. Although there is a plethora of research on computational prediction for new combination therapies, there is limited to no research on combination th...

MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations.

Journal of chemical information and modeling
Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, becaus...

SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations.

BMC bioinformatics
BACKGROUND: The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated n...

Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks.

Journal of chemical information and modeling
Botanical formulations are promising candidates for developing new biopesticides that can protect crops from pests and diseases while reducing harm to the environment. These biopesticides can be combined with permeation enhancer compounds to boost th...

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.

Biomolecules
Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships bet...

DD-PRiSM: a deep learning framework for decomposition and prediction of synergistic drug combinations.

Briefings in bioinformatics
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...

Development of a deep neural network model based on high throughput screening data for predicting synergistic estrogenic activity of binary mixtures for consumer products.

Journal of hazardous materials
A paradigm of chemical risk assessment is continuously extending from focusing on 'single substances' to more comprehensive approaches that examines the combined toxicity among different components in 'mixtures.' This change aims to account for the c...

PathSynergy: a deep learning model for predicting drug synergy in liver cancer.

Briefings in bioinformatics
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
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...