Abstract
The quantitative structure-activity relationship (QSAR) approach has been used in numerous chemical compounds as in silico computational assessment for a long time. Further, owing to the high-performance modeling of QSAR, machine learning methods have been developed and upgraded. Particularly, the three- dimensional structure of chemical compounds has been gaining increasing attention owing to the representation of a large amount of information. However, only many of feature extraction is impossible to build models with the high-ability of the prediction. Thus, suitable extraction and effective selection of features are essential for models with excellent performance. Recently, the deep learning method has been employed to construct prediction models with very high performance using big data, especially, in the field of classification. Therefore, in this study, we developed a molecular image-based novel QSAR approach, called DeepSnap-Deep learning approach for designing high-performance models. In addition, this DeepSnap-Deep learning approach outperformed the conventional machine learnings when they are compared. Herein, we discuss the advantage and disadvantages of the machine learnings as well as the availability of the DeepSnap-Deep learning approach.
Highlights
Quantitative structure-activity relationship (QSAR) is a well-established in silico approach that can predict pharmacological and toxicological effects of chemical compounds with similar structures
A 2Dmolecular descriptor is a topological or connectivity index which is a quantitative variable that characterizes topological features as an invariant for a molecular graph, for example, the topological polar surface area indicating the polar part of the surface of the molecules, Wiener index showing the sum of the shortest distances between the atoms in the molecules, and the Balaban J index exhibiting the average total bond distance in the molecules (Prasanna et al, 2005; Prasanna and Doerksen, 2009; Poša, 2011)
A 3D-molecular descriptor is a geometrical descriptors that shows 3D information of molecules, such as molecular size, molecular structure, symmetry, and atomic distribution, for example, highest occupied molecular orbital (HOMO)/lowest unoccupied molecular orbital (LUMO ) energy levels calculated from quantum chemical calculations, and a weighted holistic invariant molecular (WHIM) descriptor that is an eigenvalue calculated from a molecular matrix corresponding to a molecular graph in which 3D-coordinates are weighted based on the characteristics of each atom (Chen, 2008; Uesawa and Mohri, 2012; Marunnan et al, 2017; Elrhayam and Elharfi, 2019)
Summary
Quantitative structure-activity relationship (QSAR) is a well-established in silico approach that can predict pharmacological and toxicological effects of chemical compounds with similar structures. Machine learning to confound complex factors that contain numerous explanatory variables and construct nonlinear predictive models, it is often difficult to perform accurate modeling using conventional statistical methods.
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