AIMC Topic: Drug Combinations

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Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool.

Acta parasitologica
PURPOSE: Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation o...

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...

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...

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 ...

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...

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 ...

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...

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...

Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records.

Journal of biomedical informatics
Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Computational methods have been developed to identifying combination pharmacotherapy by analyzing large ...

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction e...