ArticleNature communications2019
A Bayesian machine learning approach for drug target identification using diverse data types.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 1 of them a synthesis that pooled it.
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
110 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mechanisms of imipridones in targeting mitochondrial metabolism in cancer cells.Neuro-oncology · 2021Pooled it
- A Phase 1 Randomized Study to Evaluate the Safety, Tolerability, and Pharmacokinetics of Single Escalating Oral Doses of Dordaviprone and the Effects of Food on the Bioavailability of Dordaviprone in Healthy Adult Subjects.Clinical pharmacology in drug development · 2025Trial
- ACTION: a randomized phase 3 study of ONC201 (dordaviprone) in patients with newly diagnosed H3 K27M-mutant diffuse glioma.Neuro-oncology · 2024Trial
- ONC201/dordaviprone for H3 K27M-mutant tumors: Discovery, mechanisms, therapeutic potential, and future directions.Genes & diseases · 2027Review
- TechBio 3.0 closes the drug discovery loop with multimodal AI and generative chemistry.npj drug discovery · 2026Review
- ONC206 demonstrates potent antitumorigenic activity and is a potential novel therapeutic strategy for high-risk medulloblastoma.Neuro-oncology · 2026Article
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Accelerated drug development using a digital formulator and a self-driving tableting data factory.Nature communications · 2026Article
- DeepMCL-DTI: predicting drug-target interactions using multi-channel deep learning with attention mechanism.Molecular diversity · 2026Article
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- A unified framework combining linear and 3D molecular features for robust drug-protein interaction prediction.Cell reports methods · 2026Article
- Curated and Structure-Based Drug-Target Interactions Improve Underprediction of Drug Side Effects in Network Models.Journal of chemical information and modeling · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Heterogeneous biological graph convolutional network for drug-target interaction prediction.PloS one · 2026Article
- The role of dordaviprone in the multidisciplinary management of diffuse midline glioma.Surgical neurology international · 2026Review
- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- Advances in cyclotide research: bioactivity to cyclotide-based therapeutics.Molecular diversity · 2025Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Advancing active compound discovery for novel drug targets: insights from AI-driven approaches.Acta pharmacologica Sinica · 2025Review
- Assessing Cytochrome P450 Drug Interaction Risk for Dordaviprone Using Physiologically Based Pharmacokinetic Modeling.CPT: pharmacometrics & systems pharmacology · 2025Article
50 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
9 authors.
Funding
Abstract
Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating public data, BANDIT benchmarked a ~90% accuracy on 2000+ small molecules. Applied to 14,000+ compounds without known targets, BANDIT generated ~4,000 previously unknown molecule-target predictions. From this set we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells. We applied BANDIT to ONC201-an anti-cancer compound in clinical development whose target had remained elusive. We identified and validated DRD2 as ONC201's target, and this information is now being used for precise clinical trial design. Finally, BANDIT identifies connections between different drug classes, elucidating previously unexplained clinical observations and suggesting new drug repositioning opportunities. Overall, BANDIT represents an efficient and accurate platform to accelerate drug discovery and direct clinical application.
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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.