几何优化算法与机器学习潜力ANI-2x一起,促进基于结构的虚拟选和绑定模式预测.
Luxuan Wang1, Xibing He1, Beihong Ji1
1Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Biomolecules
|June 27, 2024
概括
这项研究引入了一种新的计算方法,ANI-2x/CG-BS,该方法显著提高了分子对接的准确性. 改进的协议提高了识别候选药物的成功率,并完善了药物发现的结合性姿势预测.
科学领域:
- 计算化学计算化学
- 药物发现 药物发现 药物发现
- 分子建模分子建模
背景情况:
- 基于结构的虚拟查对于药物发现至关重要,但准确预测结合亲和力和姿势仍然具有挑战性.
- 现有的分子对接程序面临着对联体 - 大分子相互作用的精度限制.
- 准确预测结合方式和亲和关系对于识别可行的候选药物至关重要.
研究的目的:
- 引入一种新的计算协议,将几何优化与机器学习潜力相结合,用于增强分子对接.
- 在基于结构的虚拟选中提高绑定姿势预测和评分的准确性.
- 与现有对接方法相比,评估新协议的性能.
主要方法:
- 开发了一种新的协议,集成并联梯度与回溯线索 (CG-BS) 几何优化算法,并具有ANI-2x机器学习潜力.
- 在小分子-宏分子和-宏分子系统上应用了ANI-2x/CG-BS协议进行结构优化和潜在能量预测.
- 将该协议与Glide集成用于结合姿势预测,并评估其在优化和排名连接体方面的性能.
主要成果:
- ANI-2x/CG-BS协议证明了结合姿势的优化,特别是当初始预测具有高RMSD时.
- 与单独的Glide对接相比,在顶级排名中识别与原生相似的绑定姿势方面取得了26%的更高成功率.
- 显著增强得分和排名能力,皮尔森和斯皮尔曼的相关系数从0.24/0.14增加到0.85/0.69的连接物排名.
结论:
- 与传统方法相比,新的ANI-2x/CG-BS协议在分子对接中提供了卓越的性能.
- 这种增强的协议显示了将其集成到虚拟查管道中的巨大潜力,以加速药物发现.
- 该方法提供了更准确的结合姿势预测和改进的连接物排名,有助于识别潜在的候选药物.
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