ArticleOncotarget2017
iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A Multimodal Sequence-to-Sequence Model for Automatic Assignment of ATC Codes in Drug Discovery and Repurposing.Journal of chemical information and modeling · 2026Article
- EDRMM: enhancing drug recommendation via multi-granularity and multi-attribute representation.BMC bioinformatics · 2025Article
- Identification of Multi-functional Therapeutic Peptides Based on Prototypical Supervised Contrastive Learning.Interdisciplinary sciences, computational life sciences · 2025Article
- Prediction of drug's anatomical therapeutic chemical (ATC) code by constructing biological profiles of ATC codes.BMC bioinformatics · 2025Article
- GraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learning.Briefings in bioinformatics · 2025Article
- PMiSLocMF: predicting miRNA subcellular localizations by incorporating multi-source features of miRNAs.Briefings in bioinformatics · 2024Article
- PredictEFC: a fast and efficient multi-label classifier for predicting enzyme family classes.BMC bioinformatics · 2024Article
- RMTLysPTM: recognizing multiple types of lysine PTM sites by deep analysis on sequences.Briefings in bioinformatics · 2023Article
- i4mC-GRU: Identifying DNA NComputational and structural biotechnology journal · 2023Article
- iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features.BMC genomics · 2022Article
- A Novel Piano Arrangement Timbre Intelligent Recognition System Using Multilabel Classification Technology and KNN Algorithm.Computational intelligence and neuroscience · 2022Article
- DACPGTN: Drug ATC Code Prediction Method Based on Graph Transformer Network for Drug Discovery.Frontiers in pharmacology · 2022Article
- PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method.Cells · 2020Article
- Article
- iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties.International journal of molecular sciences · 2019Article
- Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature ExtractionCurrent genomics · 2019Article
- iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical MomentsCurrent genomics · 2019Article
- iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAACCurrent genomics · 2019Article
- ATC-NLSP: Prediction of the Classes of Anatomical Therapeutic Chemicals Using a Network-Based Label Space Partition Method.Frontiers in pharmacology · 2019Article
- A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs.Molecules (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recommended by the World Health Organization (WHO), drug compounds have been classified into 14 main ATC (Anatomical Therapeutic Chemical) classes according to their therapeutic and chemical characteristics. Given an uncharacterized compound, can we develop a computational method to fast identify which ATC class or classes it belongs to? The information thus obtained will timely help adjusting our focus and selection, significantly speeding up the drug development process. But this problem is by no means an easy one since some drug compounds may belong to two or more than two ATC classes. To address this problem, using the DO (Drug Ontology) approach based on the ChEBI (Chemical Entities of Biological Interest) database, we developed a predictor called iATC-mDO. Subsequently, hybridizing it with an existing drug ATC classifier, we constructed a predictor called iATC-mHyb. It has been demonstrated by the rigorous cross-validation and from five different measuring angles that iATC-mHyb is remarkably superior to the best existing predictor in identifying the ATC classes for drug compounds. To convenience most experimental scientists, a user-friendly web-server for iATC-mHyd has been established at http://www.jci-bioinfo.cn/iATC-mHyb, by which users can easily get their desired results without the need to go through the complicated mathematical equations involved.
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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.