Application of artificial intelligence and machine learning to study the structurebiological activity relationship
F. Sapundzhi
, S. Georgiev
, M. Lazarova
, M. Popstoilov
Резюме: Objective: This study aims to investigate the structure–biological activity relationship (SAR) of delta-opioid ligands using artificial intelligence and machine learning approaches, advancing previous efforts based on polynomial modeling. The goal is to establish nonlinear relationships between molecular docking results, binding energies, and biological activities, thereby improving predictive accuracy in drug design. Materials and methods: Three delta-opioid receptor (DOR) models were used: a theoretical model (PDB: 1ozc), a crystal structure model (PDB: 4ej4), and a homology-modeled structure. Docking studies were performed using software GOLD, and binding free energies were calculated using Molegro Molecular Viewer (MMV). Polynomial modeling was used as a baseline, while machine learning regression techniques, including k-Nearest Neighbors, Gradient Boosting, Random Forest, and Extremely Randomized Trees, were applied to capture nonlinear SARs. Model performance was evaluated using k-fold cross-validation and grid search optimization. Results: Among the tested models, Gradient Boosting exhibited the highest predictive accuracy, outperforming polynomial regression. Statistical metrics such as coefficient of determination R², root mean square error (RMSE), and sum of square errors (SSE) demonstrated the efficacy of the machine learning approach in capturing complex SARs across all three DOR models. Discussion: Machine learning algorithms provide a robust and efficient method for predicting SARs, enabling more precise identification of drug candidates. Compared to polynomial modeling, the proposed methods exhibit greater flexibility and reliability in uncovering nonlinear relationships. Conclusion: The integration of machine learning into SAR analysis enhances predictive capabilities, accelerating drug discovery and optimization. Future work will focus on extending these methods to other receptor-ligand systems and exploring additional algorithms.
Series on Biomechanics, Vol.39, No.1 (2025),74-84
DOI: 10.7546/SB.01.10.2025
Ключови думи: delta-opioid receptor; drug design; machine learning; molecular docking; Structurebiological activity relationship
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| Дата на публикуване: 2025-03-25
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