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Drug Combinations

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DeepIDC: A Prediction Framework of Injectable Drug Combination Based on Heterogeneous Information and Deep Learning.

Clinical pharmacokinetics
BACKGROUND AND OBJECTIVE: In clinical practice, injectable drug combination (IDC) usually provides good therapeutic effects for patients. Numerous clinical studies have directly indicated that inappropriate IDC generates adverse drug events (ADEs). T...

DEML: Drug Synergy and Interaction Prediction Using Ensemble-Based Multi-Task Learning.

Molecules (Basel, Switzerland)
Synergistic drug combinations have demonstrated effective therapeutic effects in cancer treatment. Deep learning methods accelerate identification of novel drug combinations by reducing the search space. However, potential adverse drug-drug interacti...

CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.

Briefings in bioinformatics
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...

Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network.

Interdisciplinary sciences, computational life sciences
Drug synergy is a crucial component in drug reuse since it solves the problem of sluggish drug development and the absence of corresponding drugs for several diseases. Predicting drug synergistic relationships can screen drug combinations in advance ...

Harmonizing across datasets to improve the transferability of drug combination prediction.

Communications biology
Combination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the respon...

MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores.

Bioinformatics (Oxford, England)
MOTIVATION: Combination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcomes. Large databases curating the results of many drug screening studies on preclinical cancer cell li...

The recent progress of deep-learning-based in silico prediction of drug combination.

Drug discovery today
Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In ...

Drug synergy model for malignant diseases using deep learning.

Journal of bioinformatics and computational biology
Drug synergy has emerged as a viable treatment option for malignancy. Drug synergy reduces toxicity, improves therapeutic efficacy, and overcomes drug resistance when compared to single-drug doses. Thus, it has attained significant interest from acad...

DeepTraSynergy: drug combinations using multimodal deep learning with transformers.

Bioinformatics (Oxford, England)
MOTIVATION: Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells.

What are the most relevant publications in Clinical Microbiology in the last two years?

Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia
This minireview describes some of the articles published in the last two years related to innovative technologies including CRISPR-Cas, surface-enhanced Raman spectroscopy, microfluidics, flow cytometry, Fourier transform infrared spectroscopy, and a...