AIMC Topic: Drug Synergism
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Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways.
Scientific reports
May 22, 2025
Breast cancer is a complex and challenging disease to treat, and despite progress in combating it, drug resistance remains a significant hindrance. Drug combinations have shown promising results in improving therapeutic outcomes, and many machine lea...
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
Feb 22, 2025
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...
Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination predictio...
Combination therapy synergism prediction for virus treatment using machine learning models.
PloS one
Sep 4, 2024
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...
MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.
Biomolecules
Aug 22, 2024
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...
SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations.
BMC bioinformatics
Jul 30, 2024
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
Jul 20, 2024
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...
MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations.
Journal of chemical information and modeling
Apr 27, 2024
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...
Prediction of anti-cancer drug synergy based on cross-matching network and cancer molecular subtypes.
Computers in biology and medicine
Apr 16, 2024
At present, anti-cancer drug synergy therapy is one of the most important methods to overcome drug resistance and reduce drug toxicity in cancer treatment. High-throughput screening through deep learning can effectively improve the efficiency of disc...
SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations.
Computational biology and chemistry
Mar 19, 2024
The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predi...
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