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This paper presents a hybrid metaheuristic algorithm, ABC-GA-VGOS, integrating Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Variable Genetic Operator Search (VGOS) for molecular docking. The algorithm initializes a population of size N, evaluates fitness using a heuristic docking energy (k-d tree with distance-based penalties), and employs adaptive crossover and mutation probabilities (pc, pm) to balance exploration and exploitation. Elitism preserves the best individual, while Solis-Wets’ local search refines solutions only when improvements are detected, optimizing computational efficiency. Experimental results on ten protein–peptide systems (1JSU, 1A1N, 1BE9, 1BXO, 1BXL, 1YCR, 2AN6, 2BBA, 2IGF, 4QVE) confirmed the robustness of the proposed method. In simpler systems such as 1JSU and 1A1N, all algorithms achieved 100% success rates (RMSD ≤ 2.0˚ A) with mean RMSDs of 0.63 ˚ A and 0.57 ˚ A, respectively. In intermediate cases (e.g., 1BE9, 2AN6, 2BBA, 2IGF, 4QVE), the hybrid algorithm consistently outperformed the baselines, yielding more negative binding energies, lower RMSDs, and higher success rates. For highly complex systems (1BXL, 1BXO, 1YCR), none of the methods succeeded (RMSD > 3.5˚ A), underscoring the challenges of flexible peptide docking. These results highlight the reliability of the proposed hybrid, making it a robust tool for protein–peptide docking, with potential for further optimization via advanced operator selection or differentiable energy models.

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1	Osmar Aguiar Ribeiro Jr(osmar.aguiar@discente.ufma.br)
2	Omar Andres Carmona Cortes(omar@ifma.edu.br)
3	João Otávio Bandeira Diniz(joaobandeira@ifma.edu.br)

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