QSAR Study of

Area of Science:

• Medicinal Chemistry
• Parasitology
• Computational Biology

Background:

• Malaria, caused by Plasmodium parasites, affects millions globally.
• Increasing drug resistance to antimalarials, including first-line treatments, highlights the urgent need for novel therapeutic agents.
• N-myristoyltransferase is a validated target for antimalarial drug development.

Purpose of the Study:

• To develop Quantitative Structure-Activity Relationship (QSAR) models for N-myristoyltransferase inhibitors.
• To identify novel drug candidates for malaria treatment by exploring structure-activity relationships.
• To provide a computational basis for designing more potent antimalarial compounds.

Main Methods:

• Application of Quantitative Structure-Activity Relationship (QSAR) methodology.
• Utilized a dataset of 83 N-myristoyltransferase inhibitors synthesized by Leatherbarrow et al.
• Developed and validated QSAR models using genetic algorithms and partial least squares (GA-PLS) with distinct alignment strategies.

Main Results:

• The best QSAR model demonstrated strong predictive performance with R²pred = 0.746 and q²adjusted = 0.634.
• The model exhibited good correlation with experimental data (r² = 0.757).
• External validation metrics (R²m = 0.716, ∆R²m = 0.133) confirmed the model's robustness.

Conclusions:

• The developed QSAR models provide a reliable framework for understanding the structural requirements of N-myristoyltransferase inhibitors.
• This study successfully identified potential new drug candidates for malaria therapy.
• The findings facilitate the rational design of novel and potent N-myristoyltransferase inhibitors to combat drug-resistant malaria parasites.