SAIAME : Semi‐Parameter Adaptation Information‐Assisted Multi‐Objective Evolutionary for Protein‐Ligand Docking
Year: 2025
Type: article
Abstract: Molecular docking, which simulates the binding pose of a drug molecule to target proteins and predicts the binding affinity, is an important computational tool in structure-based drug discovery. However, the difficulties of high ligand connectivity and dimensionality reduce the search ability of the conformational sampling. To this end, a semi-parameter adaptation information-assisted multi-objective evolution method named SAIAME is proposed for protein-ligand docking optimization. SAIAME employs a staged and dynamic semi-parameter adaptive updating strategy, in which the crossover rate is updated by a weighted arithmetic average algorithm in the exploration phase, as well as the scaling factor is updated by the Lehmer mean in the exploitation phase. It integrates a gradient enhancement based on infinity norms to smooth the decay of the weights of the learning rate during gradient descent to enhance the handling of outliers. It introduces a population size reduction strategy that combines linear and bilateral symmetric sawtooth functions to enhance its execution efficiency. The experimental results demonstrate that SAIAME not only achieves the accuracies of 87.02% for the best poses and 72.98% for the top-score poses within an RMSD of 2 Å, but also has certain advantages in execution efficiency.
Source: Chemical Biology & Drug Design
DOI: 10.1111/cbdd.70094
Authors: Wei Xiao, Haichuan Shu, Chen Xu, Wangyan Li, Juhui Ren
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409114764