ArticleChemical science2020
DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
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Who cites it
89 citing papers in PubMed, 234 citations in OpenAlex.
- Multimodal deep learning with a joint uncertainty quantification scheme for drug-target interaction prediction.Molecular diversity · 2026Article
- DeepSnap: From Three-Dimensional Molecular Images to Quantitative Structure-Activity Predictions.International journal of molecular sciences · 2026Review
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Molecular contrastive learning with graph attention network (MoCL-GAT) for enhanced molecular representation.BMC bioinformatics · 2026Article
- A comprehensive review of neural network-based approaches for drug-target interaction prediction.Molecular diversity · 2026Review
- Drug repurposing against viral infections (2020-2025): clinical trials, computational strategies, and therapeutic interventions.Inflammopharmacology · 2026Review
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Artificial Intelligence to Guide Repurposing of Drugs.Annual review of medicine · 2026Review
- Multimodal pre-training models of molecular representation for drug discovery.National science review · 2026Review
- AI-driven discovery of antiretroviral drug bictegravir and etravirine as inhibitors against monkeypox and related poxviruses.Communications biology · 2025Article
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- Conquering viral drug resistance: Structural and mechanistic paradigms for antiresistance drug design.Pharmaceutical science advances · 2025Review
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.Bioinformatics (Oxford, England) · 2025Article
- Mpox: disease manifestations and therapeutic development.Journal of virology · 2025Review
- MIF-DTI: a multimodal information fusion method for drug-target interaction prediction.Briefings in bioinformatics · 2025Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.bioRxiv : the preprint server for biology · 2025Article
- ColdstartCPI: Induced-fit theory-guided DTI predictive model with improved generalization performance.Nature communications · 2025Article
- Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction.Journal of pharmaceutical analysis · 2025Article
- Application of 3D atom pair map in an attention model for enhanced drug virtual screening.Journal of cheminformatics · 2025Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery. Since conventional screening procedures are expensive and time consuming, computational approaches are employed to provide aid by automatically predicting novel drug-target interactions (DTIs). In this study, we propose a large-scale DTI prediction system, DEEPScreen, for early stage drug discovery, using deep convolutional neural networks. One of the main advantages of DEEPScreen is employing readily available 2-D structural representations of compounds at the input level instead of conventional descriptors that display limited performance. DEEPScreen learns complex features inherently from the 2-D representations, thus producing highly accurate predictions. The DEEPScreen system was trained for 704 target proteins (using curated bioactivity data) and finalized with rigorous hyper-parameter optimization tests. We compared the performance of DEEPScreen against the state-of-the-art on multiple benchmark datasets to indicate the effectiveness of the proposed approach and verified selected novel predictions through molecular docking analysis and literature-based validation. Finally, JAK proteins that were predicted by DEEPScreen as new targets of a well-known drug cladribine were experimentally demonstrated
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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.