A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes
A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes
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DOI：
10.1039/d0cp03243c
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发表时间：
2020-09-21
影响因子：
3.3
通讯作者：
Li, Ying
中科院分区：
文献类型：
--
作者：
Chen, Guang;Shen, Zhiqiang;Li, Ying
在药物发现领域，研究有机小分子的跨膜渗透性对于设计潜在药物具有重要意义。设计有前途的药物分子的方法经历了许多阶段，从基于实验的试错方法，到成熟的定量结构-活性关系的途径，以及目前由机器学习（ML）和人工智能技术指导的阶段。在这项工作中，我们提出了两种类型的ML模型，即最小绝对收缩和选择算子（LASSO）和深度神经网络（DNN）模型的小药物样分子跨脂质膜的渗透性的研究。分子描述符和指纹用于有机分子的特征化。使用分子描述符，LASSO模型揭示了电拓扑，静电，极化率和疏水性/亲水性性质是最重要的物理性质，以确定小药物样分子的膜渗透性。此外，通过分子指纹，LASSO模型表明某些化学亚结构可以显着影响有机分子的渗透性，这与所识别的主要物理性质密切相关。此外，使用分子指纹的DNN模型可以帮助开发比LASSO模型更准确的分子结构与其膜渗透性之间的映射。我们的结果提供了深入的了解药物膜相互作用和药物样分子的反式分子设计的有用的指导。最后但并非最不重要的是，虽然目前的重点是药物样分子的渗透性，这项工作的方法是通用的，可以应用于其他复杂的物理化学问题，以获得分子的见解。
Study of the permeability of small organic molecules across lipid membranes plays a significant role in designing potential drugs in the field of drug discovery. Approaches to design promising drug molecules have gone through many stages, from experiment-based trail-and-error approaches, to the well-established avenue of the quantitative structure-activity relationship, and currently to the stage guided by machine learning (ML) and artificial intelligence techniques. In this work, we present a study of the permeability of small drug-like molecules across lipid membranes by two types of ML models, namely the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models. Molecular descriptors and fingerprints are used for featurization of organic molecules. Using molecular descriptors, the LASSO model uncovers that the electro-topological, electrostatic, polarizability, and hydrophobicity/hydrophilicity properties are the most important physical properties to determine the membrane permeability of small drug-like molecules. Additionally, with molecular fingerprints, the LASSO model suggests that certain chemical substructures can significantly affect the permeability of organic molecules, which closely connects to the identified main physical properties. Moreover, the DNN model using molecular fingerprints can help develop a more accurate mapping between molecular structures and their membrane permeability than LASSO models. Our results provide deep understanding of drug-membrane interactions and useful guidance for the inverse molecular design of drug-like molecules. Last but not least, while the current focus is on the permeability of drug-like molecules, the methodology of this work is general and can be applied for other complex physical chemistry problems to gain molecular insights.