药物样分子的高低能 conformers 的区分:对机器学习潜力和量子化学方法的评估
Linghan Kong1, Richard A Bryce1
1Division of Pharmacy and Optometry, School of Health Sciences, Manchester Academic Health Sciences Centre, University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
概括
预测连接体构造是药物发现的关键. 该GFN2-xTB方法显示承诺作为一个能源过器,准确地识别虚拟选的低能耗结构.
科学领域:
- 计算化学计算化学
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 准确预测连接体构造对于基于结构的药物发现至关重要.
- 排除不切实际的结构可以改善虚拟选和新的设计.
研究的目的:
- 评估机器学习潜力和半经验量子力学模型,用于预测结构能量学.
- 在药物发现工作流程中确定适合内部能量过器的方法.
主要方法:
- 从20种类似药物分子中构建了140个溶液构成者的数据库.
- 使用DLPNO-CCSD(T) /完整基准集 (CBS) 级别进行评估的能源.
- 评估了GFN2-xTB,ANI-2x和MACE-OFF23 (L) 模型用于结构能量预测.
主要成果:
- GFN2-xTB与参考调节器能量相关性很好 (肯德尔的t = 0.63,MAE = 2.2 kcal/mol).
- GFN2-xTB,ANI-2x和MACE-OFF23 (L) 显示出高灵敏度 (89-95%) 在识别低能规范器时.
- 这些模型的特异性降低了 (61-80%),不包括高能适配器.
结论:
- GFN2-xTB表现出最佳的整体性能,并适合作为药物发现的内部能量过器.
- 训练机器学习潜力与高能调节器可以提高他们的过能力.
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