ArticleScientific reports2017
In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D structural information.Scientific reports · 2026Article
- DDI-CYP: Metabolism ensemble models for drug-drug interaction predictions.Drug metabolism and disposition: the biological fate of chemicals · 2025Article
- Bioengineering approaches to trained immunity: Physiologic targets and therapeutic strategies.eLife · 2025Review
- Rational Design of Lipid Nanoparticles for Enhanced mRNA Vaccine Delivery via Machine Learning.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Enhancing drug repositioning: A multi-class ensemble model for drug-target interaction prediction with action type categorization.PloS one · 2025Article
- Anti-cancer activity elucidation of geissolosimine as an MDM2-p53 interaction inhibitor: An in-silico study.PloS one · 2025Article
- Predicting drug protein interactions based on improved support vector data description in unbalanced data.BioImpacts : BI · 2025Article
- The Future of Medicine: AI and ML Driven Drug Discovery Advancements.Current topics in medicinal chemistry · 2025Review
- Dynamic category-sensitive hypergraph inferring and homo-heterogeneous neighbor feature learning for drug-related microbe prediction.Bioinformatics (Oxford, England) · 2024Article
- Drug target prediction through deep learning functional representation of gene signatures.Nature communications · 2024Article
- The Experimentalist's Guide to Machine Learning for Small Molecule Design.ACS applied bio materials · 2024Review
- DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.Journal of translational medicine · 2023Article
- Article
- DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier.Interdisciplinary sciences, computational life sciences · 2022Article
- An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network.BMC bioinformatics · 2022Article
- The Discovery of New Drug-Target Interactions for Breast Cancer Treatment.Molecules (Basel, Switzerland) · 2021Article
- Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor.BMC bioinformatics · 2021Article
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- Application of Machine Learning for Drug-Target Interaction Prediction.Frontiers in genetics · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Analysis of drug-target interactions (DTIs) is of great importance in developing new drug candidates for known protein targets or discovering new targets for old drugs. However, the experimental approaches for identifying DTIs are expensive, laborious and challenging. In this study, we report a novel computational method for predicting DTIs using the highly discriminative information of drug-target interactions and our newly developed discriminative vector machine (DVM) classifier. More specifically, each target protein sequence is transformed as the position-specific scoring matrix (PSSM), in which the evolutionary information is retained; then the local binary pattern (LBP) operator is used to calculate the LBP histogram descriptor. For a drug molecule, a novel fingerprint representation is utilized to describe its chemical structure information representing existence of certain functional groups or fragments. When applying the proposed method to the four datasets (Enzyme, GPCR, Ion Channel and Nuclear Receptor) for predicting DTIs, we obtained good average accuracies of 93.16%, 89.37%, 91.73% and 92.22%, respectively. Furthermore, we compared the performance of the proposed model with that of the state-of-the-art SVM model and other previous methods. The achieved results demonstrate that our method is effective and robust and can be taken as a useful tool for predicting DTIs.
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