ArticleChemical science2018
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL.
Article in Chemical science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 181 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
181 citing papers in PubMed.
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- Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Uncoupling Type I Interferon Benefits From Inflammatory Toxicity: Transformer-Prioritized Precision Agonists for Potent and Safer Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- NMR-AI: An Open Platform for NMR-Enhanced Molecular Representations and Physicochemical Property Prediction.Journal of chemical information and modeling · 2026Article
- Epigenetic editing approaches maturity: AI-driven precision design, delivery innovation, and the road to clinical translation.Clinical epigenetics · 2026Review
- 3DICE: interpretable 3D cross-modal learning for drug-target interaction prediction and large-scale drug discovery.Bioinformatics (Oxford, England) · 2026Article
- Machine learning-driven optimization of specific, compact, and efficient base editors via single-round diversification.Nucleic acids research · 2026Article
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- HopWD-DTA: a novel framework for drug-target affinity prediction fusing multi-hop neighborhoods and deep features.Journal of molecular modeling · 2026Article
- AI-driven drug-target interaction prediction: current progress, challenges, and future roadmap for precision medicine.Journal of computer-aided molecular design · 2026Review
- A novel method for drug-target affinity prediction by integrating predicted evolutionary information and multi-scale protein graphs.BMC biology · 2026Article
- A Framework for Autonomous AI-Driven Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
- Computer-Aided Discovery of Drugs Against Chagas Disease.Methods in molecular biology (Clifton, N.J.) · 2026Article
- AI-driven molecular modeling and design: from property prediction to drug generation.Journal of computer-aided molecular design · 2025Article
- ProfhEX: Empowering Early Drug Discovery with Machine Learning-Based Target Profiling and Liability Prediction.Journal of chemical information and modeling · 2025Article
- The structure-preserving spectral graph neural network for dual kinase inhibitors and synergy scoring in gastric cancer.NPJ digital medicine · 2025Article
- A Domain Adaptive Interpretable Substructure-Aware Graph Attention Network for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
121 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
8 authors.
Funding
Abstract
Deep learning is currently the most successful machine learning technique in a wide range of application areas and has recently been applied successfully in drug discovery research to predict potential drug targets and to screen for active molecules. However, due to (1) the lack of large-scale studies, (2) the compound series bias that is characteristic of drug discovery datasets and (3) the hyperparameter selection bias that comes with the high number of potential deep learning architectures, it remains unclear whether deep learning can indeed outperform existing computational methods in drug discovery tasks. We therefore assessed the performance of several deep learning methods on a large-scale drug discovery dataset and compared the results with those of other machine learning and target prediction methods. To avoid potential biases from hyperparameter selection or compound series, we used a nested cluster-cross-validation strategy. We found (1) that deep learning methods significantly outperform all competing methods and (2) that the predictive performance of deep learning is in many cases comparable to that of tests performed in wet labs (
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.