ReviewMedical review (2021)2023
An overview of recent advances and challenges in predicting compound-protein interaction (CPI).
Review in Medical review (2021), 2023. 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
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
8 citing papers in PubMed.
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- Molegro Data Modeller for Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Generalizable compound protein interaction prediction with a model incorporating protein structure aware and compound property aware language model representations.Communications chemistry · 2025Article
- Mapping protein-metabolite interactions iniScience · 2025Article
- BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions.Journal of chemical theory and computation · 2025Article
- Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics.Cell genomics · 2024Article
- Accelerating drug discovery and repurposing by combining transcriptional signature connectivity with docking.Science advances · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.
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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.