AIMC Topic: Network Pharmacology
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MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction.
BMC bioinformatics
Aug 23, 2024
BACKGROUND: The rise of network pharmacology has led to the widespread use of network-based computational methods in predicting drug target interaction (DTI). However, existing DTI prediction models typically rely on a limited amount of data to extra...
Novel drug discovery: Advancing Alzheimer's therapy through machine learning and network pharmacology.
European journal of pharmacology
May 23, 2024
Alzheimer's disease (AD), marked by tau tangles and amyloid-beta plaques, leads to cognitive decline. Despite extensive research, its complex etiology remains elusive, necessitating new treatments. This study utilized machine learning (ML) to analyze...
Prediction of quality markers in Maren Runchang pill for constipation using machine learning and network pharmacology.
Molecular omics
May 7, 2024
Maren Runchang pill (MRRCP) is a Chinese patent medicine used to treat constipation in clinics. It has multi-component and multi-target characteristics, and there is an urgent need to screen markers to ensure its quality. The aim of this study was to...
Integrating network pharmacology, molecular docking and simulation approaches with machine learning reveals the multi-target pharmacological mechanism of against diabetic nephropathy.
Journal of biomolecular structure & dynamics
Feb 20, 2024
Diabetic nephropathy (DN) is one of the most feared complications of diabetes and key cause of end-stage renal disease (ESRD). has been widely used to treat diabetic complications, but exact molecular mechanism is yet to be discovered. Data on activ...
Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities.
Drug discovery today
Nov 13, 2023
With increasing human life expectancy, the global medical burden of chronic diseases is growing. Hence, chronic diseases are a pressing health concern and will continue to be in decades to come. Chronic diseases often involve multiple malfunctioning ...
Quantitative systems pharmacology in the age of artificial intelligence.
CPT: pharmacometrics & systems pharmacology
Oct 1, 2023
Deep learning-based network pharmacology for exploring the mechanism of licorice for the treatment of COVID-19.
Scientific reports
Apr 10, 2023
Licorice, a traditional Chinese medicine, has been widely used for the treatment of COVID-19, but all active compounds and corresponding targets are still not clear. Therefore, this study proposed a deep learning-based network pharmacology approach t...
Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides.
Journal of bioinformatics and computational biology
Dec 14, 2022
In this study, we propose , a system that aims to computationally generate novel antiviral peptides (AVPs). This system relies on the key premise of the Generative Adversarial Network (GAN) model and the Feedback method. GAN, a generative modeling ap...
FordNet: Recommending traditional Chinese medicine formula via deep neural network integrating phenotype and molecule.
Pharmacological research
Sep 2, 2021
Traditional Chinese medicine (TCM) formula is widely used for thousands of years in clinical practice. With the development of artificial intelligence, deep learning models may help doctors prescribe reasonable formulas. Meanwhile, current studies of...
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