ReviewCurrent pharmaceutical design2014
Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles.
Review in Current pharmaceutical design, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
81 citing papers in PubMed.
- Metabolic Activation-Dependent Anti-Liver Cancer Activity and Organ-Specific Toxicity of Retrorsine From Fanhuncao (Jacobaea cannabifolia and J. litvinovii).Archiv der Pharmazie · 2026Article
- Essentiality-driven prediction of anticancer drug responses in preclinical and clinical contexts.iScience · 2026Article
- Nootkatone as a multi-target modulator of the PD-L1/PD-1 signaling in pancreatic cancer: network pharmacology, docking, molecular dynamics, and DFT analysis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Exploration of network-based, machine learning, MD simulations, and MM/GBSA approaches revealed luteolin from Gynura procumbens as key inhibitor of MMP9 in NSCLC.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Review
- Apigenin as a Multi-Targeted Agent in Gastrointestinal Cancers: A Systems Pharmacology Approach.Advanced pharmaceutical bulletin · 2026Article
- The MOGA multi-modal framework based on graph augmentation networks for drug response prediction.iScience · 2026Article
- Deciphering the molecular mechanism of sesamol in inflammatory bowel disease through an integrative computational and biological evaluation of the JAK1 signalling pathway.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Unraveling the anti-neuroinflammatory mechanisms of Cervus cucumis polypeptide injection in Alzheimer's disease: insights from network pharmacology, molecular docking, molecular dynamics simulation, and experimental validation.Frontiers in aging neuroscience · 2026Article
- Investigation and Mechanism of Coumarin for Potential Anti-Epileptic Targets usingCNS & neurological disorders drug targets · 2026Article
- Analysis of the interaction network relationship between drugs using a graph neural network.Frontiers in pharmacology · 2026Article
- Molecular immunopharmacology of traditional Chinese medicine-derived compounds in membranous nephropathy: mechanistic insights into immune aging and kidney essence deficiency.Frontiers in immunology · 2026Review
- Evaluating current status of network pharmacology for herbal medicine focusing on identifying mechanisms and therapeutic effects.Journal of advanced research · 2025Article
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- Dual-loaded niosome-dendrimer nanoplatform enhances Tirapazamine delivery to hypoxic breast cancer cells.Scientific reports · 2025Article
- AI-Driven Polypharmacology in Small-Molecule Drug Discovery.International journal of molecular sciences · 2025Review
- Integrative network pharmacology and experimental validation reveal emodin derivatives as potential therapeutics for hepatocellular carcinoma.Journal of the Egyptian National Cancer Institute · 2025Article
- Ferulic acid as a promising candidate for developing selective and effective anti-cancer therapies.Discover oncology · 2025Review
- Diabetes-compound Relationship Identification based on Complex-valued Flexible Neural Tree and Negative Sample Selection Algorithm.Current computer-aided drug design · 2025Article
- Elucidating Anti-aging and Antioxidant Activity ofCombinatorial chemistry & high throughput screening · 2025Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.