期刊
COMPUTATIONAL BIOLOGY AND CHEMISTRY
卷 89, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compbiolchem.2020.107377
关键词
Dimensionality reduction; Chemoinformatics; Blood-brain barrier (BBB) permeability; Quantitative Structure Activity Relationships (QSAR); Recurrent Neural Networks (RNN); Kernel PCA
资金
- Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program
- Deanship of Scientific Research, King Saud University through Vice Deanship of Scientific Research Chairs
The rapid development of computational methods and the increasing volume of chemical and biological data have contributed to an immense growth in chemical research. This field of study is known as chemoinformatics, which is a discipline that uses machine-learning techniques to extract, process, and extrapolate data from chemical structures. One of the significant lines of research in chemoinformatics is the study of blood-brain barrier (BBB) permeability, which aims to identify drug penetration into the central nervous system (CNS). In this research, we attempt to solve the problem of BBB permeability by predicting compounds penetration to the CNS. To accomplish this goal: (i) First, an overview is provided to the field of chemoinformatics, its definition, applications, and challenges, (ii) Second, a broad view is taken to investigate previous machine-learning and deep-learning computational models to solve BBB permeability. Based on the analysis of previous models, three main challenges that collectively affect the classifier performance are identified, which we define as the triple constraints; subsequently, we map each constraint to a proposed solution, (iii) Finally, we conclude this endeavor by proposing a deep learning based Recurrent Neural Network model, to predict BBB permeability (RNN-BBB model). Our model outperformed other studies from the literature by scoring an overall accuracy of 96.53%, and a specificity score of 98.08%. The obtained results confirm that addressing the triple constraints substantially improves the classification model capability specifically when predicting compounds with low penetration.
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