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

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ID: 277803
2021
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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.
Reference Key
choi2021exploringscientific Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Choi, Jiwon;Yun, Jun Seop;Song, Hyeeun;Kim, Nam Hee;Kim, Hyun Sil;Yook, Jong In;
Journal Scientific reports
Year 2021
DOI
13369
URL
Keywords
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