ArticlePLoS computational biology2010
Drug off-target effects predicted using structural analysis in the context of a metabolic network model.
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers.
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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
109 citing papers in PubMed, 208 citations in OpenAlex.
- Selecting methods for draft GEM generation in multicellular eukaryotes: a comparative analysis.BMC bioinformatics · 2026Article
- NTMFF-DTA: Prediction of Drug-Target Affinity Based on Network Topology and Multi-feature Fusion.Interdisciplinary sciences, computational life sciences · 2025Article
- Genome-scale models in human metabologenomics.Nature reviews. Genetics · 2025Review
- Small molecules targeting microRNAs: new opportunities and challenges in precision cancer therapy.Trends in cancer · 2024Review
- Machine Learning Prediction of On/Off Target-driven Clinical Adverse Events.Pharmaceutical research · 2024Article
- Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases.Metabolites · 2024Review
- Pipeline for characterizing alternative mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity.Computational and structural biotechnology journal · 2023Article
- Roles of Cofactors in Drug-Induced Liver Injury: Drug Metabolism and Beyond.Drug metabolism and disposition: the biological fate of chemicals · 2022Review
- Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors.Genomics, proteomics & bioinformatics · 2021Article
- Importance of the biomass formulation for cancer metabolic modeling and drug prediction.iScience · 2021Article
- Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model.Toxicology and applied pharmacology · 2021Article
- Mechanisms of Action for Small Molecules Revealed by Structural Biology in Drug Discovery.International journal of molecular sciences · 2020Review
- iMM1865: A New Reconstruction of Mouse Genome-Scale Metabolic Model.Scientific reports · 2020Article
- Big Data and Artificial Intelligence Modeling for Drug Discovery.Annual review of pharmacology and toxicology · 2020Review
- Epidrug Repurposing: Discovering New Faces of Old Acquaintances in Cancer Therapy.Frontiers in oncology · 2020Review
- Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine.Frontiers in bioengineering and biotechnology · 2020Review
- Expanding the uses of genome-scale models with protein structures.Molecular systems biology · 2019Article
- DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks.Bioinformatics (Oxford, England) · 2019Article
- Drug repurposing with network reinforcement.BMC bioinformatics · 2019Article
- Genome-scale metabolic model of the rat liver predicts effects of diet restriction.Scientific reports · 2019Article
49 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine.
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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.