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LiProS:可查找,可访问,可互操作和可重复使用的数据模拟工作流程,用于准确预测小分子的脂性概况

Esteban Bertsch-Aguilar^1,2, Antonio Piedra², Daniel Acuña¹

¹CBio3 Laboratory, School of Chemistry, University of Costa Rica, San Pedro, Costa Rica.

Molecular informatics

|August 26, 2025

概括

我们开发了LiProS,一个FAIR工作流程,用于使用SMILES代码预测pH依赖的脂性特征. 这种工具有助于研究人员在药物设计和材料科学中选择合适的脂性模型.

科学领域:

计算化学
化学信息学
药物发现

背景情况:

脂性是影响药物吸收,分布和生物分子相互作用的关键物理化学性质.
对于药物设计和材料科学来说,准确预测pH依赖的脂性 (log D) 是至关重要的.
现有的方法可能无法充分捕捉可电离化合物的细微差别.

研究的目的:

引入LiProS,一个FAIR (可查找,可访问,可互操作,可重复使用) 的工作流程,用于确定pH依赖的脂性特征.
为研究人员提供基于SMILES代码的可访问工具来预测脂性.
为各种化学化合物选择合适的脂性形式.

主要方法:

通过 Google Colab 访问的 LiProS 工作流程的开发.
使用SMILES代码作为分子表示的输入.
纳入离子明显分离系数 (P_I^app) 以提高pH依赖性预测的准确性.
应用到NAPRORE-CR天然产品数据库进行电离化合物的分析.

主要成果:

根据SMILES代码,LiProS可以有效地确定pH依赖的脂性特征.
这项工作流程在分析可电离化合物方面具有实用性.
对于特定的化合物集,LiProS 便于识别合适的脂性形式.

结论:

LiProS提供了一个用户友好且符合FAIR的解决方案来预测脂友性.
该工具提高了pH依赖性脂友性建模的准确性,特别是对于可电离化合物.
支持科学研究中的数据管理和共享原则.

关键词:

公平的化学信息学疏水性肥胖性机器学习

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