ArticleComputational and structural biotechnology journal2023
The application of machine learning methods to the prediction of novel ligands for ROR
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Machine Learning Application for Medicinal Chemistry: Colchicine Case, New Structures, and Anticancer Activity Prediction.Pharmaceuticals (Basel, Switzerland) · 2024Article
- QSPRmodeler - An open source application for molecular predictive analytics.Frontiers in bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this work, we developed and applied a computational procedure for creating and validating predictive models capable of estimating the biological activity of ligands. The combination of modern machine learning methods, experimental data, and the appropriate setup of molecular descriptors led to a set of well-performing models. We thoroughly inspected both the methodological space and various possibilities for creating a chemical feature space. The resulting models were applied to the virtual screening of the ZINC20 database to identify new, biologically active ligands of ROR
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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.