AIMC Topic: Drug Interactions

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Effects of Jiazhu decoction in combination with cyclophosphamide on breast cancer in mice.

Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan
OBJECTIVE: To investigate the therapeutic effects of Jiazhu decoction (JZD) in combination with cyclophosphamide (CTX) on the growth of breast cancer in mice and to explore the possible molecular mechanisms of action.

Artificial intelligence in drug combination therapy.

Briefings in bioinformatics
Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a...

Drug knowledge bases and their applications in biomedical informatics research.

Briefings in bioinformatics
Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many...

Principi di farmacodinamica e farmacocinetica nello switch tra antipsicotici: focus su cariprazina.

Rivista di psichiatria
Cariprazina {RGH-188; trans-N- [4- [2- [4- (2,3-diclorofenil) piperazin-1-il] etil] cicloesil] -N_, N_-dimetilurea cloridrato} รจ un antipsicotico atipico di nuova generazione, con un originale profilo farmacodinamico e farmacocinetico. Cariprazina ha...

Computational Prediction of Drug-Target Interactions via Ensemble Learning.

Methods in molecular biology (Clifton, N.J.)
Therapeutic effects of drugs are mediated via interactions between them and their intended targets. As such, prediction of drug-target interactions is of great importance. Drug-target interaction prediction is especially relevant in the case of drug ...

A Drug Repurposing Method Based on Drug-Drug Interaction Networks and Using Energy Model Layouts.

Methods in molecular biology (Clifton, N.J.)
Complex network representations of reported drug-drug interactions foster computational strategies that can infer pharmacological functions which, in turn, create incentives for drug repositioning. Here, we use Gephi (a platform for complex network v...

A chronological pharmacovigilance network analytics approach for predicting adverse drug events.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they t...

Nonlinear System Identification Based on Convolutional Neural Networks for Multiple Drug Interactions.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In heart failure patients, hemodynamics can be regulated by therapeutic drugs. Although the cardiovascular responses to these drugs usually include nonlinearity and drug interactions, it is difficult to identify the characteristics of the dynamics un...

Modeling polypharmacy side effects with graph convolutional networks.

Bioinformatics (Oxford, England)
MOTIVATION: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Po...

DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.

Bioinformatics (Oxford, England)
MOTIVATION: Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate.