AIMC Topic: Drug Synergism
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Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.
Scientific reports
Jan 18, 2024
Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the ...
Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool.
Acta parasitologica
Jan 2, 2024
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...
Drug synergy model for malignant diseases using deep learning.
Journal of bioinformatics and computational biology
Jun 22, 2023
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...
MatchMaker: A Deep Learning Framework for Drug Synergy Prediction.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2022
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...
TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.
PLoS computational biology
Feb 12, 2021
Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer drugs has caused the experimental investigation of all drug combinations...
H-RACS: a handy tool to rank anti-cancer synergistic drugs.
Aging
Nov 10, 2020
Though promising, identifying synergistic combinations from a large pool of candidate drugs remains challenging for cancer treatment. Due to unclear mechanism and limited confirmed cases, only a few computational algorithms are able to predict drug s...
Evaluation of magnesium oxide and zinc oxide nanoparticles against multi-drug-resistance Mycobacterium tuberculosis.
The Indian journal of tuberculosis
Aug 4, 2020
OBJECTIVE: The current study has evaluated the MICs and MBCs of ZnONPs, MgONPs, and MgONPs-ZnONPs against HRv Mtb and MDR-Mtb.
Combination Therapy of Clinically Approved Antifungal Drugs Is Enhanced by Conjugation with Silver Nanoparticles.
International microbiology : the official journal of the Spanish Society for Microbiology
Nov 21, 2018
Silver nanoparticles (SN) have been recently developed as a new class of antimicrobial agents against numerous pathogenic microorganisms. SN have also been used as efficient drug delivery systems and have been linked with increasing drug potency. Her...
Predict effective drug combination by deep belief network and ontology fingerprints.
Journal of biomedical informatics
Aug 3, 2018
The synergistic effect of drug combination is one of the most desirable properties for treating cancer. However, systematically predicting effective drug combination is a significant challenge. We report here a novel method based on deep belief netwo...
A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria.
Malaria journal
Apr 11, 2018
BACKGROUND: Nearly half of the world's population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identif...
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