AIMC Topic: Drug Combinations

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Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.

Journal of translational medicine Jun 16, 2024
BACKGROUND: Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction be...
Neural Networks, Computer Reproducibility of Results Humans Drug Interactions Drug-Related Side Effects and Adverse Reactions Computer Graphics Risk Assessment Drug Combinations
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An Artificial Neural Network Predicts Gender Differences of Motor and Non-Motor Symptoms of Patients with Advanced Parkinson's Disease under Levodopa-Carbidopa Intestinal Gel.

Medicina (Kaunas, Lithuania) May 26, 2024
: Currently, no tool exists to predict clinical outcomes in patients with advanced Parkinson's disease (PD) under levodopa-carbidopa intestinal gel (LCIG) treatment. The aim of this study was to develop a novel deep neural network model to predict th...
Female Aged Humans Neural Networks, Computer Quality of Life Severity of Illness Index Male Middle Aged Carbidopa Sex Factors Longitudinal Studies Gels Antiparkinson Agents Parkinson Disease Levodopa Treatment Outcome Drug Combinations
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MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations.

Journal of chemical information and modeling Apr 27, 2024
Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, becaus...
Deep Learning Cell Line, Tumor Drug Combinations Drug Synergism Humans Antineoplastic Agents
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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 ...
Machine Learning Computational Biology Drug Therapy, Combination Drug Combinations Drug Synergism
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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 Synergism Drug Therapy, Combination Machine Learning Drug Combinations Humans Plasmodium falciparum Antimalarials Malaria, Falciparum
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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 Nov 24, 2023
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...
beta-Lactamases Ceftazidime Drug Combinations Flow Cytometry Anti-Bacterial Agents Microbial Sensitivity Tests Humans Artificial Intelligence
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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...
Deep Learning Computational Biology Artificial Intelligence Drug Combinations Drug Synergism
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The recent progress of deep-learning-based in silico prediction of drug combination.

Drug discovery today May 25, 2023
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 Combinations Algorithms Deep Learning
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Harmonizing across datasets to improve the transferability of drug combination prediction.

Communications biology Apr 11, 2023
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...
Machine Learning Drug Discovery Drug Combinations High-Throughput Screening Assays
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Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network.

Interdisciplinary sciences, computational life sciences Mar 21, 2023
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 ...
Neural Networks, Computer Drug Combinations
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