ArticleJournal of cheminformatics2022
Random-forest model for drug-target interaction prediction via Kullbeck-Leibler divergence.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 40 citations in OpenAlex.
- IHDFN-DTI: Interpretable Hybrid Deep Feature Fusion Network for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- LNMGAT: a laplacian regularized pseudo-negative mining graph attention network for robust drug-target interaction prediction under multi-scenario cold-start settings.Frontiers in bioinformatics · 2026Article
- Artificial Intelligence to Guide Repurposing of Drugs.Annual review of medicine · 2026Review
- MIF-DTI: a multimodal information fusion method for drug-target interaction prediction.Briefings in bioinformatics · 2025Article
- ElixirSeeker: A Machine Learning Framework Utilizing Fusion Molecular Fingerprints for the Discovery of Lifespan-Extending Compounds.Aging cell · 2025Article
- QKDTI A quantum kernel based machine learning model for drug target interaction prediction.Scientific reports · 2025Article
- Applications of Artificial Intelligence in Drug Repurposing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Identification of novel inflammatory response-related biomarkers in patients with ischemic stroke based on WGCNA and machine learning.European journal of medical research · 2025Article
- Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review.npj biomedical innovations · 2025Review
- Machine learning enables construction of a nomogram based on risk factors for adverse emotions in patients with diabetic foot infection.American journal of translational research · 2025Article
- Article
- Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.Computational and structural biotechnology journal · 2024Review
- NO classifier prediction of anti neuroinflammatory agents using text mining of 3D molecular fingerprints.Scientific reports · 2024Article
- Hierarchical multimodal self-attention-based graph neural network for DTI prediction.Briefings in bioinformatics · 2024Article
- Virtual Screening of Small Molecules Targeting BCL2 with Machine Learning, Molecular Docking, and MD Simulation.Biomolecules · 2024Article
- GPCR-IPL score: multilevel featurization of GPCR-ligand interaction patterns and prediction of ligand functions from selectivity to biased activation.Briefings in bioinformatics · 2024Article
- Correction : Random-forest model for drug-target interaction prediction via Kullback-Leibler divergence.Journal of cheminformatics · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Virtual screening has significantly improved the success rate of early stage drug discovery. Recent virtual screening methods have improved owing to advances in machine learning and chemical information. Among these advances, the creative extraction of drug features is important for predicting drug-target interaction (DTI), which is a large-scale virtual screening of known drugs. Herein, we report Kullbeck-Leibler divergence (KLD) as a DTI feature and the feature-driven classification model applicable to DTI prediction. For the purpose, E3FP three-dimensional (3D) molecular fingerprints of drugs as a molecular representation allow the computation of 3D similarities between ligands within each target (Q-Q matrix) to identify the uniqueness of pharmacological targets and those between a query and a ligand (Q-L vector) in DTIs. The 3D similarity matrices are transformed into probability density functions via kernel density estimation as a nonparametric estimation. Each density model can exploit the characteristics of each pharmacological target and measure the quasi-distance between the ligands. Furthermore, we developed a random forest model from the KLD feature vectors to successfully predict DTIs for representative 17 targets (mean accuracy: 0.882, out-of-bag score estimate: 0.876, ROC AUC: 0.990). The method is applicable for 2D chemical similarity.
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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.