ArticleScientific reports2021
Exploring the chemical space of protein-protein interaction inhibitors through machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Efficient visualization of chemical space.Chemical science · 2026Article
- Targeting "undruggable" cancer proteins: pharmacological challenges and emerging strategies.Translational cancer research · 2026Review
- Machine Learning Models to Predict Protein-Protein Interaction Inhibitors.Molecules (Basel, Switzerland) · 2022Article
- In Silico Prediction ofPharmaceuticals (Basel, Switzerland) · 2022Article
- A Selective Inhibitor of Cardiac Troponin I Phosphorylation by Delta Protein Kinase C (δPKC) as a Treatment for Ischemia-Reperfusion Injury.Pharmaceuticals (Basel, Switzerland) · 2022Article
- IFI44 is an immune evasion biomarker for SARS-CoV-2 andFrontiers in immunology · 2022Article
- Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although protein-protein interactions (PPIs) have emerged as the basis of potential new therapeutic approaches, targeting intracellular PPIs with small molecule inhibitors is conventionally considered highly challenging. Driven by increasing research efforts, success rates have increased significantly in recent years. In this study, we analyze the physicochemical properties of 9351 non-redundant inhibitors present in the iPPI-DB and TIMBAL databases to define a computational model for active compounds acting against PPI targets. Principle component analysis (PCA) and k-means clustering were used to identify plausible PPI targets in regions of interest in the active group in the chemical space between active and inactive iPPI compounds. Notably, the uniquely defined active group exhibited distinct differences in activity compared with other active compounds. These results demonstrate that active compounds with regions of interest in the chemical space may be expected to provide insights into potential PPI inhibitors for particular protein targets.
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Registered trials
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