ArticlePloS one2014
Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 70 citations in OpenAlex.
- A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics.Life (Basel, Switzerland) · 2025Article
- Article
- Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method.Journal of chemical information and modeling · 2023Article
- pADR: Towards Personalized Adverse Drug Reaction Prediction by Modeling Multi-sourced Data.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2023Article
- Pretrained transformer models for predicting the withdrawal of drugs from the market.Bioinformatics (Oxford, England) · 2023Article
- An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects.Artificial intelligence review · 2023Article
- Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effects.Frontiers in drug safety and regulation · 2023Article
- Machine learning prediction of side effects for drugs in clinical trials.Cell reports methods · 2022Article
- Study on the Characteristics of Small-Molecule Kinase Inhibitors-Related Drug-Induced Liver Injury.Frontiers in pharmacology · 2022Article
- Prediction of adverse drug reactions based on knowledge graph embedding.BMC medical informatics and decision making · 2021Article
- An up-to-date overview of computational polypharmacology in modern drug discovery.Expert opinion on drug discovery · 2020Review
- Broad-Spectrum Profiling of Drug Safety via Learning Complex Network.Clinical pharmacology and therapeutics · 2020Article
- A platform for target prediction of phenotypic screening hit molecules.Journal of molecular graphics & modelling · 2020Article
- Biological representation of chemicals using latent target interaction profile.BMC bioinformatics · 2019Article
- Inferring new relations between medical entities using literature curated term co-occurrences.JAMIA open · 2019Article
- Molecular Docking: Shifting Paradigms in Drug Discovery.International journal of molecular sciences · 2019Review
- Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects.Nature communications · 2019Article
- Predicting adverse drug reactions through interpretable deep learning framework.BMC bioinformatics · 2018Article
- Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach.Molecules (Basel, Switzerland) · 2018Article
- A chronological pharmacovigilance network analytics approach for predicting adverse drug events.Journal of the American Medical Informatics Association : JAMIA · 2018Article
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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.
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.