论文标题
通过分子特征建模的化学化合物的血脑屏障通透性的硅预测
In Silico Prediction of Blood-Brain Barrier Permeability of Chemical Compounds through Molecular Feature Modeling
论文作者
论文摘要
用于分析化学数据的计算技术的引入引起了对生物系统的分析研究，即“生物信息学”。生物信息学的一个方面是使用机器学习（ML）技术在各种情况下检测多变量趋势。最紧迫的情况之一是预测血脑屏障（BBB）的渗透性。治疗中枢神经系统疾病的新药物的开发带来了独特的挑战，因为整个血脑屏障的渗透功效不佳。在这项研究中，我们旨在通过分析化学特征的ML模型来减轻此问题。这样做：（i）给出了相关的生物系统和过程以及用例的概述。 （ii）第二，对检测BBB渗透性的现有计算技术进行了深入的文献综述。从那里开始，确定了跨电流技术的一个方面，并提出了解决方案。 （iii）最后，开发，测试和反映出通过被动扩散在BBB中定义特征在BBB中定义特征的药物的渗透性的两部分，以量化渗透率。使用数据集进行的测试和验证确定了预测的LogBB模型的平方误差约为0.112单位，而神经炎症模型的平方误差约为0.3个单位，表现优于所有相关研究。
The introduction of computational techniques to analyze chemical data has given rise to the analytical study of biological systems, known as "bioinformatics". One facet of bioinformatics is using machine learning (ML) technology to detect multivariable trends in various cases. Amongst the most pressing cases is predicting blood-brain barrier (BBB) permeability. The development of new drugs to treat central nervous system disorders presents unique challenges due to poor penetration efficacy across the blood-brain barrier. In this research, we aim to mitigate this problem through an ML model that analyzes chemical features. To do so: (i) An overview into the relevant biological systems and processes as well as the use case is given. (ii) Second, an in-depth literature review of existing computational techniques for detecting BBB permeability is undertaken. From there, an aspect unexplored across current techniques is identified and a solution is proposed. (iii) Lastly, a two-part in silico model to quantify likelihood of permeability of drugs with defined features across the BBB through passive diffusion is developed, tested, and reflected on. Testing and validation with the dataset determined the predictive logBB model's mean squared error to be around 0.112 units and the neuroinflammation model's mean squared error to be approximately 0.3 units, outperforming all relevant studies found.
