Advancements in QSAR modelling: Decision trees and rotation forest for prediction of Aspergillus anti-inflammatory metabolites

This study presents applications of advancements in QSAR modelling for predicting nitric oxide (NO) inhibitors and anti-inflammatory metabolites from the Aspergillus genus. Inflammation-related diseases remain a pressing concern, necessitating the identification of effective anti-inflammatory compounds. Using decision trees, the Ranker method, and CorrelationAtrributeEval as a base classifier for attribute selection together with Rotation Forest and Adaboost as enhancers, we explored their potential with different classifiers including Artificial Neural Networks and J48 Trees. The proposed QSAR models employed an ensemble approach with Rotation Forest and Adaboost.M1, applying an automated KNIME workflow. Seven molecular descriptors were selected and trained on a comprehensive dataset of diverse anti-inflammatory Aspergillus specialised metabolites. Results showed that the Rotation Forest-enhanced version outperformed other models, capturing complex structure-activity relationships and improving predictive performance. Chemical characteristics of electrotopological state, topological distances, and functional groups including secondary amides and alcohols contribute to important anti-inflammatory effects. The developed QSAR model showed good predictive performance for anti-inflammatory Aspergillus metabolites, focusing on their NO inhibitory activity. These results can contribute to the discovery of novel anti-inflammatory drugs based on computational techniques.