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

Jiwon Choi, Jun Seop Yun, Hyeeun Song, Nam Hee Kim, Hyun Sil Kim, Jong In Yook

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Article number13369
JournalScientific reports
Volume11
Issue number1
DOIs
Publication statusPublished - 2021 Dec

Bibliographical note

Funding Information:
This work was supported by grants from the National Research Foundation of Korea (NRF-2016R1E1A1A01942724, NRF-2017R1A2B3002241, NRF-2018R1D1A1B07050744, and NRF-2019R1A2C2084535) funded by the Korea government (MSIP).

Publisher Copyright:
© 2021, The Author(s).

All Science Journal Classification (ASJC) codes

  • General

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