Computational drug discovery approach based on nuclear factor-κB pathway dynamics

Ky Youb Nam, Won Seok Oh, Chul Kim, Miyoung Song, Jong Young Joung, Sunyoung Kim, Jaeseong Park, Sin Moon Gang, Young Uk Cho, Kyoung Tai No

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The NF-κB system of transcription factors plays a crucial role in inflammatory diseases, making it an important drug target. We combined quantitative structure activity relationships for predicting the activity of new compounds and quantitative dynamic models for the NF-κB network with intracellular concentration models. GFA-MLR QSAR analysis was employed to determine the optimal QSAR equation. To validate the predictability of the IKKb QSAR model for an external set of inhibitors, a set of ordinary differential equations and mass action kinetics were used for modeling the NF-κB dynamic system. The reaction parameters were obtained from previously reported research. In the IKKb QSAR model, good cross-validated q 2 (0.782) and conventional r 2 (0.808) values demonstrated the correlation between the descriptors and each of their activities and reliably predicted the IKKβ activities. Using a developed simulation model of the NF-κB signaling pathway, we demonstrated differences in IκB mRNA expression between normal and different inhibitory states. When the inhibition efficiency increased, inhibitor 1 (PS-1145) led to long-term oscillations. The combined computational modeling and NF-κB dynamic simulations can be used to understand the inhibition mechanisms and thereby result in the design of mechanism-based inhibitors.

Original languageEnglish
Pages (from-to)4397-4402
Number of pages6
JournalBulletin of the Korean Chemical Society
Issue number12
Publication statusPublished - 2011 Dec 20

All Science Journal Classification (ASJC) codes

  • Chemistry(all)


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