AIMC Topic: Network Pharmacology
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In-silico exploring pathway and mechanism-based therapeutics for allergic rhinitis: Network pharmacology, molecular docking, ADMET, quantum chemistry and machine learning based QSAR approaches.
Computers in biology and medicine
Feb 4, 2025
Allergic rhinitis is a devastating health complication that interrupts the quality of daily life and significantly affects around 40 % of the population worldwide. Despite the availability of various treatment options, many patients continue to strug...
Unveiling the molecular mechanisms of Haitang-Xiaoyin Mixture in psoriasis treatment based on bioinformatics, network pharmacology, machine learning, and molecular docking verification.
Computational biology and chemistry
Jan 15, 2025
OBJECTIVE: Psoriasis is a common clinical skin inflammatory disease. Haitang-Xiaoyin Mixture (HXM) represents a traditional Chinese medicine formulation utilized clinically for the management of psoriasis, which can reduce the psoriasis area and seve...
Unveiling key drivers of hepatocellular carcinoma: a synergistic approach with network pharmacology, machine learning-driven ligand discovery and dynamic simulations.
SAR and QSAR in environmental research
Jan 3, 2025
Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. This study aims to uncover the genes and pathways involved in HCC through network pharmacology (NP) and to discover potential drugs via machine learning (ML)-based lig...
AlzyFinder: A Machine-Learning-Driven Platform for Ligand-Based Virtual Screening and Network Pharmacology.
Journal of chemical information and modeling
Oct 31, 2024
Alzheimer's disease (AD), a prevalent neurodegenerative disorder, presents significant challenges in drug development due to its multifactorial nature and complex pathophysiology. The AlzyFinder Platform, introduced in this study, addresses these cha...
Combining network pharmacology, machine learning, molecular docking and molecular dynamic to explore the mechanism of Chufeng Qingpi decoction in treating schistosomiasis.
Frontiers in cellular and infection microbiology
Sep 6, 2024
BACKGROUND: Although the Chufeng Qingpi Decoction (CQD) has demonstrated clinical effectiveness in the treatment of schistosomiasis, the precise active components and the underlying mechanisms of its therapeutic action remain elusive. To achieve a pr...
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 ...
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