An integrated approach to predict activators of NRF2 - the transcription factor for oxidative stress response

Abstract

A variety of environmental and physiological conditions can cause oxidative stress that damage cellular components such as DNA, proteins and lipids. Oxidative stress is implicated in many human diseases including cancer, cardiovascular diseases, neurological diseases, inflammatory diseases, and aging. The nuclear factor erythroid 2–related factor 2 (NRF2) is a transcriptional factor that plays a key role in the cellular antioxidant defense system as it regulates transcription of antioxidant proteins and detoxifying enzymes. There is an urgent need to identify novel compounds that activate NRF2 and enhance antioxidant defense. We collected data from the high-throughput screening of NRF2 activators and identified molecular fragments (structural alerts) associated with the activation of NRF2. We also developed ten classification models using different types of molecular descriptors and machine learning techniques. Two approaches were used to establish the applicability domain of developed models: the structure-based approach and the distance to model approach. The best performing model that used message passing neural network (MPNN) technique showed accuracy of 87 % for the test set of chemicals within the distance to model of 0.3. The integrative approach using a combination of generated structural alerts and MPNN model was used to screen approved drugs collected in the DrugBank to identify potential NRF2 activators. Out of 2393 screened chemicals 138 compounds were predicted as NRF2 activators by both approaches. Analysis of these compounds showed that some drugs were already known activators of NRF2 while others are potentially novel activators.

Keywords

• Cellular response to oxidative stress
• NRF2 activation
• Molecular fragments
• Machine learning modeling
• Classification models