ArticleBriefings in bioinformatics2015
Toward more realistic drug-target interaction predictions.
Article in Briefings in bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 224 papers, 1 of them a synthesis that pooled it.
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Who cites it
224 citing papers in PubMed, 1 synthesis or guideline pooled it, 543 citations in OpenAlex.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- CDELDA: A Content-Based Dual-Encoder for Cold-Start lncRNA-Disease Association Prediction.Biomedicines · 2026Article
- Genome-scale perturbation signatures from primary human CD4bioRxiv : the preprint server for biology · 2026Article
- KAN-GIN: Adaptive Nonlinear Molecular Representation Learning for Drug-Target Affinity Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Confidence-Gated Triage: Coupling Drug-Target Affinity and ADME-T Predictions to Prioritise Compounds for Docking.Pharmaceuticals (Basel, Switzerland) · 2026Article
- GSF-DTA: An Innovative Graph-Sequence Fusion Framework for Drug-Target Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Multimodal deep learning with a joint uncertainty quantification scheme for drug-target interaction prediction.Molecular diversity · 2026Article
- Evaluation of deep learning architectures for predicting ligand interactions with neurologically relevant GPCRs.Journal of cheminformatics · 2026Article
- Article
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Automated Processes and Artificial Intelligence in Generating Candidates for Oncology Drug Repurposing: Three-Year Scoping Review of Data.Pharmacy (Basel, Switzerland) · 2026Review
- Bidirectional cross-modal fusion with tensor interaction for drug-target binding prediction.Scientific reports · 2026Article
- DTANet+: Dual Interaction and Kernel-Diverse Network for Drug-Target Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D structural information.Scientific reports · 2026Article
- An Agentic Platform for Drug Repurposing Unified across Molecular, Phenotypic, and Clinical Scales.bioRxiv : the preprint server for biology · 2026Article
- GraphTransDTA: Drug-Target Affinity Prediction with Graph Transformer for Multimodal Data Fusion.Interdisciplinary sciences, computational life sciences · 2026Article
- Out-of-distribution generalization enhances protein function annotation for low-homology sequences.Briefings in bioinformatics · 2026Article
- TSEDTA: a transformer-based neural network with SMILES transformer and ESM2 embeddings for drug-target binding affinity prediction.Bioinformatics (Oxford, England) · 2026Article
- DCI-SiteDTA: drug-target affinity prediction based on binding sites detection and site-aware dual cross-interaction block.BMC bioinformatics · 2026Article
- Results of a Large-Scale Study of the Binding of 50 Type II Inhibitors to 348 Kinases: The Role of Protein Reorganization.Journal of medicinal chemistry · 2026Article
164 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A number of supervised machine learning models have recently been introduced for the prediction of drug-target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction models are often being constructed and evaluated under overly simplified settings that do not reflect the real-life problem in practical applications. Using quantitative drug-target bioactivity assays for kinase inhibitors, as well as a popular benchmarking data set of binary drug-target interactions for enzyme, ion channel, nuclear receptor and G protein-coupled receptor targets, we illustrate here the effects of four factors that may lead to dramatic differences in the prediction results: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither). Each of these factors should be taken into consideration to avoid reporting overoptimistic drug-target interaction prediction results. We also suggest guidelines on how to make the supervised drug-target interaction prediction studies more realistic in terms of such model formulations and evaluation setups that better address the inherent complexity of the prediction task in the practical applications, as well as novel benchmarking data sets that capture the continuous nature of the drug-target interactions for kinase inhibitors.
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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.