AIMC Topic: Drug Synergism

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Path-Based Graph Neural Network for Drug Synergy Prediction and Interpretation.

Journal of chemical information and modeling
Combination therapy is a method of treating complex diseases by using multiple drugs, which has the advantages of good efficacy and few toxic side effects. It has been widely used in clinical research. The significant increase in the number of drug c...

GLA-Synergy: An Interpretable Global-Local Adaptive Framework for Drug Synergy Prediction in Cancer Treatment.

Journal of chemical information and modeling
Effective anticancer drug combinations are crucial for advancing cancer treatment, yet predicting drug synergy remains challenging due to the complexity of biological interactions. Existing methods struggle to integrate multimodal features and to mod...

VCTatDot and VCTatMLP: novel deep learning models with triadic attention embeddings for synergistic drug combination prediction.

Scientific reports
Computational drug repurposing is vital in drug discovery research because it significantly reduces both the cost and time involved in the drug development process. Additionally, combination therapy-using more than one drug for treatment-can enhance ...

Synergistic analgesic effects of astaxanthin combined with celecoxib on a mouse bone cancer pain model: From behavioral validation to target prediction.

International immunopharmacology
Bone cancer pain (BCP) is a complex condition that severely affects patients' quality of life, and its treatment remains challenging. Astaxanthin, a potent antioxidant with anti-inflammatory and neuroprotective effects, and celecoxib, a selective COX...

DGSS: A Dynamic Interaction Graph Neural Network with Specific Substructure Awareness for Drug Synergy Prediction.

Journal of chemical information and modeling
Combination therapy presents a transformative approach to treating complex diseases such as cancer by mitigating toxicity and resistance challenges inherent to monotherapy. A critical gap in current computational methods, however, lies in their inabi...

Integrated transcriptomic and functional modeling reveals AKT and mTOR synergy in colorectal cancer.

Scientific reports
Colorectal cancer (CRC) treatment remains challenging due to genetic heterogeneity and resistance mechanisms. To address this, we developed a drug discovery pipeline using patient-derived primary CRC cultures with diverse genomic profiles. These cult...

Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays.

Science advances
Combination therapies enhance the therapeutic effect of cancer treatment; however, identifying effective interdependent doses, durations, and sequences of multidrug administration regimens is a time- and labor-intensive task. Here, we integrated mach...

Accurate prediction of synergistic drug combination using a multi-source information fusion framework.

BMC biology
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...

Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways.

Scientific reports
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
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...