ArticleJournal of cheminformatics2020
Quantitative prediction of selectivity between the A
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Prediction of Activity and Selectivity Profiles of Sigma Receptor Ligands Using Machine Learning Approaches.Journal of chemical information and modeling · 2025Article
- Beyond Tanimoto: a learned bioactivity similarity index enhances ligand discovery.Frontiers in bioinformatics · 2025Article
- Pmf-cpi: assessing drug selectivity with a pretrained multi-functional model for compound-protein interactions.Journal of cheminformatics · 2023Article
- AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology.Chemical science · 2023Article
- UnCorrupt SMILES: a novel approach to de novo design.Journal of cheminformatics · 2023Article
- pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures.Bioinformatics advances · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The development of drugs is often hampered due to off-target interactions leading to adverse effects. Therefore, computational methods to assess the selectivity of ligands are of high interest. Currently, selectivity is often deduced from bioactivity predictions of a ligand for multiple targets (individual machine learning models). Here we show that modeling selectivity directly, by using the affinity difference between two drug targets as output value, leads to more accurate selectivity predictions. We test multiple approaches on a dataset consisting of ligands for the A
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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.