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Updated: May 15, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
Published on: May 9, 2025
Development of predictive quantitative structure-activity relationship model and its application in the discovery of
Sundarapandian Thangapandian1, Shalini John, Minky Son
1Division of Applied Life Science (BK21 Program), Systems & Synthetic Agrobiotech Center, Research Institute of Natural Science, Plant Molecular Biology & Biotechnology Research Center, Gyeongsang National University, 501 Jinju-daero, Gazwa-dong, Jinju 660-701, Republic of Korea.
Background:
Human LTA4H catalyzes the conversion of LTA4 to LTB4 and plays a key role in innate immune responses. Inhibition of this enzyme can be a valid method in the treatment of inflammatory response exhibited through LTB4.
Results & Discussion:
The quantitative structure-activity relationship (QSAR) models were developed using genetic function approximation and validated. A training set of 26 diverse compounds and their molecular descriptors were used to develop highly correlating QSAR models. A six-descriptor model explaining the biological activity of the training and test sets with correlation values of 0.846 and 0.502, respectively, was selected as the best model and used in a database screening of drug-like Maybridge database followed by molecular docking.
Conclusion:
Based on the predicted potent inhibitory activities, expected binding mode and molecular interactions at the active site of hLTA4H final leads were selected as to be utilized in designing future hLTA4H inhibitors.
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