ArticleBioinformatics (Oxford, England)2018
A novel methodology on distributed representations of proteins using their interacting ligands.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- GCNPath: introspecting drug response prediction with pathway-guided graph convolution networks.Communications biology · 2026Article
- DualPG-DTA: A Large Language Model-Powered Graph Neural Network Framework for Enhanced Drug-Target Affinity Prediction and Discovery of Novel CDK9 Inhibitors Exhibiting In Vivo Anti-Leukemia Activity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- Beyond the leaderboard: leveraging predictive modeling for protein-ligand insights and discovery.Bioinformatics (Oxford, England) · 2025Article
- Domain adaptable language modeling of chemical compounds identifies potent pathoblockers for Pseudomonas aeruginosa.Communications chemistry · 2025Article
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- Article
- Protein feature engineering framework for AMPylation site prediction.Scientific reports · 2024Article
- PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity.Journal of cheminformatics · 2023Article
- Gene expression based inference of cancer drug sensitivity.Nature communications · 2022Article
- Organizing the bacterial annotation space with amino acid sequence embeddings.BMC bioinformatics · 2022Article
- An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph.Briefings in bioinformatics · 2022Review
- Deep Neural Network-Assisted Drug Recommendation Systems for Identifying Potential Drug-Target Interactions.ACS omega · 2022Article
- PIGNet: a physics-informed deep learning model toward generalized drug-target interaction predictions.Chemical science · 2022Article
- Article
- Machine Learning in Antibacterial Drug Design.Frontiers in pharmacology · 2022Review
- Transformational machine learning: Learning how to learn from many related scientific problems.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug-Target Interaction Model.Cognitive computation · 2021Article
- Identifying DNA N4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation.Computational and structural biotechnology journal · 2021Article
- iN6-methylat (5-step): identifying DNA NMolecular genetics and genomics : MGG · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: The effective representation of proteins is a crucial task that directly affects the performance of many bioinformatics problems. Related proteins usually bind to similar ligands. Chemical characteristics of ligands are known to capture the functional and mechanistic properties of proteins suggesting that a ligand-based approach can be utilized in protein representation. In this study, we propose SMILESVec, a Simplified molecular input line entry system (SMILES)-based method to represent ligands and a novel method to compute similarity of proteins by describing them based on their ligands. The proteins are defined utilizing the word-embeddings of the SMILES strings of their ligands. The performance of the proposed protein description method is evaluated in protein clustering task using TransClust and MCL algorithms. Two other protein representation methods that utilize protein sequence, Basic local alignment tool and ProtVec, and two compound fingerprint-based protein representation methods are compared. Results: We showed that ligand-based protein representation, which uses only SMILES strings of the ligands that proteins bind to, performs as well as protein sequence-based representation methods in protein clustering. The results suggest that ligand-based protein description can be an alternative to the traditional sequence or structure-based representation of proteins and this novel approach can be applied to different bioinformatics problems such as prediction of new protein-ligand interactions and protein function annotation. Availability and implementation: https://github.com/hkmztrk/SMILESVecProteinRepresentation. Supplementary information: Supplementary data are available at Bioinformatics online.
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.