在药物发现中估计被动透性的计算方法的进步
Austen Bernardi1, W F Drew Bennett1, Stewart He1
1Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
计算方法正在推进通过细胞膜的被动透性的预测,这对于药物开发至关重要. 机器学习显示出对物质如何跨越生物障碍的高效,数据驱动的洞察力有希望.
科学领域:
- 药理学和药物发现
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 细胞膜的被动透对于药物输送和理解生物系统至关重要.
- 生物膜对于所有生物实体的细分至关重要.
- 准确预测被动透性对于治疗开发至关重要.
研究的目的:
- 审查目前用于表征被动透性的计算技术.
- 讨论透性预测中的理论和最近的应用.
- 突出机器学习在这个领域的潜力.
主要方法:
- 审查已建立的方法,如脂性关系和分子动力学模拟.
- 探索新兴的机器学习方法,用于透性预测.
- 对不均溶解度扩散模型的讨论.
主要成果:
- 不同的计算技术在准确性,复杂性和成本方面提供了不同的权衡.
- 机器学习模型显示出大量数据驱动的透性预测的巨大潜力.
- 计算能力的进步正在使更复杂的分析成为可能.
结论:
- 计算方法对于预测被动透性越来越重要.
- 机器学习为透性评估提供了一种强大的数据驱动方法.
- 透性预测的未来是由新的计算方法和超级计算驱动的.
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