ArticleJournal of chemical information and modeling2019
STarFish: A Stacked Ensemble Target Fishing Approach and its Application to Natural Products.
Article in Journal of chemical information and modeling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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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
25 citing papers in PubMed, 51 citations in OpenAlex.
- Real-time validation of advanced control strategies for grid-connected PV-wind hybrid systems using adaptive starfish and greedy man optimization algorithms.Scientific reports · 2026Article
- Review
- ComTarget: Small-Molecule Target Prediction with Combinatorial Modeling.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Artificial intelligence-based screening of phytochemicals for targeted cancer therapy.Natural products and bioprospecting · 2026Review
- Rethinking Nature's Pharmacy: AI Era and Natural Product Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Artificial Intelligence for Natural Products Drug Discovery in Neurodegenerative Therapies: A Review.Biomolecules · 2026Review
- Target identification of natural products in cancer with chemical proteomics and artificial intelligence approaches.Cancer biology & medicine · 2025Review
- Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.Journal of medicinal chemistry · 2025Review
- KinasePred: A Computational Tool for Small-Molecule Kinase Target Prediction.International journal of molecular sciences · 2025Article
- Machine learning-based prediction of glioma grading.PloS one · 2025Article
- Comprehensive applications of the artificial intelligence technology in new drug research and development.Health information science and systems · 2024Review
- Natural product databases for drug discovery: Features and applications.Pharmaceutical science advances · 2024Review
- Latin American Natural Product Database (LANaPDB): An Update.Journal of chemical information and modeling · 2024Article
- Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products.ACS synthetic biology · 2023Review
- In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences.Journal of cheminformatics · 2023Article
- Cheminformatics and artificial intelligence for accelerating agrochemical discovery.Frontiers in chemistry · 2023Review
- Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time.IEEE transactions on biomedical circuits and systems · 2022Article
- Natural product drug discovery in the artificial intelligence era.Chemical science · 2022Review
- Three diketomorpholines from a Penicillium sp. (strain G1071).Phytochemistry · 2021Article
- Recent Advances in In Silico Target Fishing.Molecules (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Target fishing is the process of identifying the protein target of a bioactive small molecule. To do so experimentally requires a significant investment of time and resources, which can be expedited with a reliable computational target fishing model. The development of computational target fishing models using machine learning has become very popular over the last several years because of the increased availability of large amounts of public bioactivity data. Unfortunately, the applicability and performance of such models for natural products has not yet been comprehensively assessed. This is, in part, due to the relative lack of bioactivity data available for natural products compared to synthetic compounds. Moreover, the databases commonly used to train such models do not annotate which compounds are natural products, which makes the collection of a benchmarking set difficult. To address this knowledge gap, a data set composed of natural product structures and their associated protein targets was generated by cross-referencing 20 publicly available natural product databases with the bioactivity database ChEMBL. This data set contains 5589 compound-target pairs for 1943 unique compounds and 1023 unique targets. A synthetic data set comprising 107 190 compound-target pairs for 88 728 unique compounds and 1907 unique targets was used to train
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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.