机器学习方法作为一种成本效益高的替代方案,以物理为基础的自由能量计算
Nupur Bansal1, Ye Wang1, Simone Sciabola1
1Biotherapeutic and Medicinal Sciences, Biogen, 225 Binney Street, Cambridge, MA 02142, USA.
Molecules (Basel, Switzerland)
|February 24, 2024
概括
通过比较基于物理和机器学习的药物发现方法,这项研究发现基于物理的方法在溶解变化方面表现出色,而机器学习则提供了一个依赖于训练数据质量的成本有效的替代方案,用于结合自由能量预测.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 在药理学中的机器学习.
背景情况:
- 准确预测蛋白质 - 配体结合亲和力对于药物发现至关重要.
- 基于物理学的方法面临的挑战是力场准确性和 conformational 采样.
- 机器学习 (ML) 方法正在成为绑定亲和力预测的强大工具.
研究的目的:
- 以后期评估和比较基于最先进的物理和机器学习方法的性能,以进行具有约束力的自由能量计算.
- 评估不同蛋白质标和带系列的不同计算方法的优点和局限性.
主要方法:
- 对172种化合物进行了追溯的自由能量结合评估,涉及四种蛋白质标和五种同源带系列.
- 对比包括各种基于物理的方法,复杂度和采样不同,以及现有的最先进的机器学习模型.
- 绩效的评估是基于等级排序配体的准确性和预测结合亲缘关系.
主要成果:
- 基于物理学的方法在带扰动涉及溶解区域时表现出强的性能.
- 基于物理学的方法在准确捕捉蛋白质活性位点内的大形状变化方面存在局限性.
- 基于机器学习的方法提供了一个具有成本效益的替代方案,但它们的预测准确性取决于可用的培训数据的质量和数量.
结论:
- 基于物理学的和机器学习的方法在预测蛋白质-联体结合的自由能量方面都有明显的优势和局限性.
- 在选择方法时,应考虑系统的特定特征,例如连接体扰动的性质和形状灵活性.
- 机器学习模型显示出作为高效工具的承诺,强调需要强大的实验数据集,以便可靠的模型培训和药物发现中的应用.
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