ArticleACS omega2022
Machine Learning-Assisted Prediction of the Biological Activity of Aromatase Inhibitors and Data Mining to Explore Similar Compounds.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- Machine learning based approaches for structure activity relationship analysis of heparanase inhibitors.Scientific reports · 2026Article
- In Silico Identification of Natural-Product Aromatase (CYP19A1) Inhibitors via Integrated Molecular Docking and QSAR Modelling.Bioinformatics and biology insights · 2026Article
- Machine Learning-Guided Design of Rhenium Tricarbonyl Complexes for Next-Generation Antibiotics.ACS bio & med chem Au · 2025Article
- Chemotaxonomy, an Efficient Tool for Medicinal Plant Identification: Current Trends and Limitations.Plants (Basel, Switzerland) · 2025Review
- Integrated Virtual Screening Approach Identifies New CYP19A1 Inhibitors.Journal of chemical information and modeling · 2025Article
- QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Article
- Performance prediction of polymer-fullerene organic solar cells and data mining-assisted designing of new polymers.Journal of molecular modeling · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Designing molecules for drugs has been a hot topic for many decades. However, it is hard and expensive to find a new molecule. Thus, the cost of the final drug is also increased. Machine learning can provide the fastest way to predict the biological activity of druglike molecules. In the present work, machine learning models are trained for the prediction of the biological activity of aromatase inhibitors. Data was collected from the literature. Molecular descriptors are calculated to be used as independent features for model training. The results showed that the
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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.