开发分子对接的通用分数函数:挑战和前景
Rodrigo Quiroga1, Marcos Villarreal1
1Departamento de Química Teórica y Computacional, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba. Instituto de Investigaciones en Físico-Química de Córdoba, INFIQC-CONICET, Argentina.
Current medicinal chemistry
|November 1, 2024
概括
开发精确的评分函数 (SF) 对于基于结构的药物发现至关重要. 本综述强调了培训SF的最佳实践,重点关注数据质量和强大的方法论,以提高药物标结合性亲和力预测.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 药理学 药理学是指药理学的学科.
背景情况:
- 基于结构的药物发现依赖于分子对接和虚拟选.
- 评分函数 (SFs) 对于预测蛋白质-连接体结合亲缘关系至关重要.
研究的目的:
- 审查在培训新型评分功能的挑战和最佳实践.
- 提高SFs的准确性和通用性,以预测约束亲和关系.
主要方法:
- 审查现有的关于评分功能的训练的文献.
- 重点是数据质量,偏差缓解和过度匹配的预防.
- 探索混合经验和机器学习方法.
主要成果:
- 有效的SF培训需要高质量,公正的数据集.
- 需要强大的培训策略来实现一致和可通用的SF.
- 混合方法显示出卓越性能和多功能性的潜力.
结论:
- 解决偏差和过拟合对于可靠的SFs至关重要.
- 高质量的数据对于成功的SF开发至关重要.
- 创新的混合评分功能为药物发现提供了有前途的进展.
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