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Updated: Jul 4, 2026
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
Published on: May 9, 2025
Data-Driven Structure-Property Analysis and Rational-Efficient Retrosynthetic Design of
Ismail Dwi Putra1, Thanawadi Yutthaphatsak2, Nicha Khachitwiwat2
1Center of Excellence in Structural and Computational Biology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
Abstract:
Understanding how molecular structure governs functional performance is essential for data-driven design of bioactive heterocycles. Here, a series of 1,2-dihydronaphtho-[2,1-b]-furan-2-ylmethanone derivatives was analyzed to establish quantitative structure-property relationships associated with cytotoxic performance in MCF-7 and MDA-MB-468 systems. Interpretable machine learning models were benchmarked across five algorithms, with Gradient Boosting Regression and Random Forest providing the highest predictive accuracy (R 2training = 0.968 and 0.943, respectively). SHAP attribution identified electrotopological and lipophilic descriptors, particularly VSA_EState3 and BCUTdv-1l, as dominant determinants governing activity. These insights enabled data-driven generation of 48 derivatives, prioritizing compound 6D as a high-performance candidate with favorable predicted ADME properties. Target network analysis and docking indicated consistent interaction with the HSP90AA1 hydrophobic pocket. Retrosynthetic assessment further supported synthetic accessibility and reduced step complexity. Overall, this work links descriptors, bioactivity, and synthesis feasibility within an interpretable framework for structure-guided optimization of 1,2-dihydronaphtho-[2,1-b]-furan derivatives.
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