ReviewBriefings in bioinformatics2021
Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.
Review in Briefings in bioinformatics, 2021. 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 152 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
152 citing papers in PubMed.
- AI-enabled engineering of hesperidin/ursodeoxycholic acid nanomedicine for synergistic treatment of drug-induced liver injury.Smart molecules : open access · 2026Article
- AI-driven drug design: a comprehensive review.Journal of computer-aided molecular design · 2026Review
- Artificial Intelligence in Pharmaceutical Care:Saudi medical journal · 2026Review
- Deep Contrastive Learning for High-Throughput Prediction of Drug Resistance Mutations from Sequences.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- An electron-density point-cloud framework for robust protein-ligand interaction prediction.Nature communications · 2026Article
- Combinatorial prediction of therapeutic perturbations using causally inspired neural networks.Nature biomedical engineering · 2026Article
- Computer-aided drug design in acute myeloid leukemia: a comprehensive review of advances, challenges, and future prospect.Journal of computer-aided molecular design · 2026Review
- Multi-to-uni modal knowledge transfer pre-training for molecular representation learning.Nature communications · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Real-time visualization of drug-target interactions in native subcellular microenvironments for lysosome-targeted drug discovery.Journal of pharmaceutical analysis · 2026Article
- Comparing Neural Networks and Naive Bayes in the Prediction of Drug Gene Interactions of Type 4 Collagenase for Gingival Epithelialization.International journal of dentistry · 2026Article
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Semi-inductive dataset construction and framework optimization for practical drug target interaction prediction with ScopeDTI.Nature communications · 2025Article
- WDGBANDTI: A Deep Graph Convolutional Network-Based Bilinear Attention Network for Drug-Target Interaction Prediction with Domain Adaptation.Interdisciplinary sciences, computational life sciences · 2025Article
- Multimodal out-of-distribution individual uncertainty quantification enhances binding affinity prediction for polypharmacology.Nature machine intelligence · 2025Article
- An interpretable geometric graph neural network for enhancing the generalizability of drug-target interaction prediction.BMC biology · 2025Article
- Decoupled dual-granularity rebalanced pyramid network for drug-target interaction prediction.Briefings in bioinformatics · 2025Article
- Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- MGPT: A Multi-task Graph Prompt Learning Framework for Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Artificial intelligence and computational methods in human metabolism research: A comprehensive survey.Journal of pharmaceutical analysis · 2025Review
92 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug-target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.
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.