ArticleJournal of chemical information and modeling2024
APIPred: An XGBoost-Based Method for Predicting Aptamer-Protein Interactions.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 7 citations in OpenAlex.
- AptViralDB: A repository of experimentally validated antiviral aptamers.Molecular therapy. Nucleic acids · 2026Review
- Nucleic acid aptamers: new methods for selection, target validation, molecular diagnostics and therapeutics.Signal transduction and targeted therapy · 2026Review
- DeepAptamer: Advancing high-affinity aptamer discovery with a hybrid deep learning model.Molecular therapy. Nucleic acids · 2025Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 3 countries.
Funding
Abstract
Aptamers are single-stranded DNA or RNA oligos that can bind to a variety of targets with high specificity and selectivity and thus are widely used in the field of biosensing and disease therapies. Aptamers are generated by SELEX, which is a time-consuming procedure. In this study, using in silico and computational tools, we attempt to predict whether an aptamer can interact with a specific protein target. We present multiple data representations of protein and aptamer pairs and multiple machine-learning-based models to predict aptamer-protein interactions with a fair degree of selectivity. One of our models showed 96.5% accuracy and 97% precision, which are significantly better than those of the previously reported models. Additionally, we used molecular docking and SPR binding assays for two aptamers and the predicted targets as examples to exhibit the robustness of the APIPred algorithm. This reported model can be used for the high throughput screening of aptamer-protein pairs for targeting cancer and rapidly evolving viral epidemics.
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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.