使用神经网络势增强蛋白质-配体结合亲和力预测
Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials.
作者信息
Zariquiey Francesc Sabanes, Galvelis Raimondas, Gallicchio Emilio, Chodera John D, Markland Thomas E, de Fabritiis Gianni
出版信息
ArXiv. 2024 Feb 14:arXiv:2401.16062v2.
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies (RBFE) with the Alchemical Transfer Method (ATM) and validate its performance against established benchmarks and find significant enhancements compared to conventional MM force fields like GAFF2.
这封信给出了基于分子动力学模拟，使用具有混合神经网络势和分子力学方法（NNP/MM）的机器学习势来改进蛋白质-配体结合亲和力预测的结果。我们用炼金术转移方法（ATM）计算相对结合自由能（RBFE），并对照既定基准验证其性能，结果发现与GAFF2等传统分子力学力场相比有显著提升。
Zotero 插件