ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Biomolecular Interaction Prediction: The Era of AI.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
10 citing papers in PubMed.
- Artificial Intelligence as a Discovery Engine for Routine Molecular Techniques: Extracting Biological Insight from Western Blotting, ELISA, Immunostaining, and Immunoprecipitation.Cell biochemistry and biophysics · 2026Review
- Bridging Nano-Interface Interactions and Organ-Specific Toxicity: A Review of Machine Learning for Nanomaterials Risk Assessment.Molecules (Basel, Switzerland) · 2026Review
- Ultrasound-guided closed-loop control of magnetic hydrogel microrobots with adaptive gait switching.Microsystems & nanoengineering · 2026Article
- Article
- The interplay between the pyramidalization of carbonyl carbons and the n → π* interactions in biomolecules.Protein science : a publication of the Protein Society · 2026Article
- Review
- Artificial Intelligence as a Disruptive Force in Pharmaceutical Innovation: Transforming Discovery, Development, and Manufacturing.Drug design, development and therapy · 2026Review
- Special Issue "Biomolecular Structure, Function and Interactions".International journal of molecular sciences · 2025Article
- AI-Integrated Micro/Nanorobots for Biomedical Applications: Recent Advances in Design, Fabrication, and Functions.Biosensors · 2025Review
- Biomolecular Interaction Prediction: The Era of AI.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
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.
Funding
Abstract
Predicting biomolecular interactions is a crucial task in drug discovery and molecular biology. Deep learning, with its ability to learn complex patterns from large datasets, has shown promising results in predicting biomolecular interactions. In this review, a comprehensive and accessible overview of deep learning algorithms is aimed to provide that can enhance the prediction of biomolecular interactions using various features, including sequence data, structural information, and functional annotations. The datasets and models for predicting biomolecular interactions using deep learning are summarized. These deep learning models are developed for a wide range of target molecules, including proteins, nucleic acids, and small molecules, thus reducing the time and cost of screening compounds with high binding affinity to a given target. Furthermore, deep learning can also aid in understanding the mechanisms of biomolecular interactions by identifying key residues involved in the interaction, and help in predicting the side effects of drugs by identifying potential off-target interactions. In conclusion, deep learning has the potential to revolutionize drug discovery and improve understanding of molecular biology by providing accurate and efficient prediction in biomolecular interactions.
Indexed as
Identifiers
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.