ArticleJournal of chemical information and modeling2017
Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.
Article in Journal of chemical information and modeling, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 74 citations in OpenAlex.
- Hepatotoxicity prediction for traditional Chinese medicine: a two-step in silico framework integrating network and machine learning approaches.BMC complementary medicine and therapies · 2026Article
- Model-Informed Drug Development: Addressing the Critical Need for Training in the Promising New Field.Delaware journal of public health · 2026Article
- AΙ-Driven Drug Repurposing: Applications and Challenges.Medicines (Basel, Switzerland) · 2025Review
- Pathway-based network medicine identifies novel natural products for Alzheimer's disease.Alzheimer's research & therapy · 2025Article
- Network-based identification and mechanism exploration of active ingredients against Alzheimer's disease via targeting endoplasmic reticulum stress from traditional chinese medicine.Computational and structural biotechnology journal · 2024Article
- Drug repurposing for cancer therapy.Signal transduction and targeted therapy · 2024Review
- Systems pharmacology dissection of pharmacological mechanisms of Xiaochaihu decoction against human coronavirus.BMC complementary medicine and therapies · 2023Article
- Restricting Promiscuity of Plant Flavonoid 3'-Hydroxylase and 4'-Journal of agricultural and food chemistry · 2023Article
- Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs.Marine drugs · 2023Review
- Methodology of network pharmacology for research on Chinese herbal medicine against COVID-19: A review.Journal of integrative medicine · 2022Review
- Molecular dynamics simulations, docking and MMGBSA studies of newly designed peptide-conjugated glucosyloxy stilbene derivatives with tumor cell receptors.Molecular diversity · 2022Article
- Natural products for infectious microbes and diseases: an overview of sources, compounds, and chemical diversities.Science China. Life sciences · 2022Review
- wSDTNBI: a novel network-based inference method for virtual screening.Chemical science · 2022Article
- Chemical Distance Measurement and System Pharmacology Approach Uncover the Novel Protective Effects of Biotransformed Ginsenoside C-Mc against UVB-Irradiated Photoaging.Oxidative medicine and cellular longevity · 2022Article
- In Silico Identification and Mechanism Exploration of Active Ingredients against Stroke from An-Gong-Niu-Huang-Wan (AGNHW) Formula.Oxidative medicine and cellular longevity · 2022Article
- Regulation of the PTEN/PI3K/AKT pathway in RCC using the active compounds of natural productsMolecular medicine reports · 2021Article
- Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor.BMC bioinformatics · 2021Article
- In silico identification of natural products from Traditional Chinese Medicine for cancer immunotherapy.Scientific reports · 2021Review
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- Network-Based Identification and Experimental Validation of Drug Candidates Toward SARS-CoV-2Frontiers in genetics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.