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Exploring the chemical space of protein-protein interaction inhibitors through machine learning

Authors
 Jiwon Choi  ;  Jun Seop Yun  ;  Hyeeun Song  ;  Nam Hee Kim  ;  Hyun Sil Kim  ;  Jong In Yook 
Citation
 SCIENTIFIC REPORTS, Vol.11(1) : 13369, 2021-06 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2021-06
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.
Files in This Item:
T202102757.pdf Download
DOI
10.1038/s41598-021-92825-5
Appears in Collections:
2. College of Dentistry (치과대학) > Research Institute (부설연구소) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Oral Pathology (구강병리학교실) > 1. Journal Papers
Yonsei Authors
Kim, Nam Hee(김남희) ORCID logo https://orcid.org/0000-0002-3087-5276
Kim, Hyun Sil(김현실) ORCID logo https://orcid.org/0000-0003-3614-1764
Yook, Jong In(육종인) ORCID logo https://orcid.org/0000-0002-7318-6112
Choi, Jiwon(최지원)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184295
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