Genetic algorithm-guided design of garlic-derived ligands targeting the TEN domain of telomerase reverse transcriptase with machine learning-based activity classification

Human telomerase reverse transcriptase (hTERT) plays a key role in cancer cell immortalization and represents an important therapeutic target for anticancer drug discovery. In this study, a computational pipeline combining genetic algorithms (GAs) and machine learning (ML) was developed to design and screen garlic-derived bioactive compounds as potential telomerase inhibitors. Garlic phytochemicals were used as the initial chemical space, which was iteratively evolved through mutation and fragment expansion to generate novel ligand candidates. A multi-parameter fitness function incorporating Lipinski, Veber, and Ghose drug-likeness rules, quantitative estimate of drug-likeness (QED), hydrogen bond donor/acceptor balance, and aromatic ring constraints was used to guide optimization. In addition, a RandomForest-based classifier was applied to pre-screen compounds for predicted telomerase activity prior to molecular docking. The shortlisted ligands were evaluated using CB-Dock2, and further assessed for pharmacokinetic and toxicity properties using SwissADME, including solubility, lipophilicity, and bioavailability. Out of 125 generated ligands, 14 met both drug-likeness and predicted activity criteria and progressed through the full pipeline. The highest binding affinity observed was -10.5 kcal/mol; however, this top-scoring compound was excluded due to ADMET rule violations. The remaining candidates exhibited favorable physicochemical properties, acceptable solubility, balanced lipophilicity, and good predicted oral bioavailability. Overall, the results demonstrate that genetic algorithms can efficiently generate structurally diverse and pharmacologically relevant scaffolds, and that integrating an activity-based machine learning filter prior to docking improves screening efficiency by prioritizing biologically meaningful candidates. Compared with traditional GA-based docking workflows, this integrated strategy provides a more selective and cost-effective approach for early-stage ligand discovery.