A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning

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.

Automated summary

The QSAR approach has been widely used for a long time, with the development of deep learning methods showing excellent performance in modeling, especially in the field of classification. Proper feature extraction and selection are crucial for building models with outstanding performance.