TY  - JOUR
T1  - Prediction of Compound-Protein Interactions Using Deep Learning
AU  - Seo, Sangmin 
AU  - Ahn, Jaegyoon 
JO  - Journal of KIISE, JOK
PY  - 2019
DA  - 2019/1/14
DO  - 10.5626/JOK.2019.46.10.1054
KW  - Machine translation
KW  - deep learning
KW  - drug development
KW  - compound-protein interaction
KW  - classification
AB  - Characterizing the interactions between compounds and proteins is an important process for drug development and discovery. Structural data of proteins and compounds are used to identify their interactions, but those structural data are not always available, and the speed and accuracy of the predictions made in this way ware limited due to the large number of calculations involved. In this paper, compound-protein interactions were predicted using S2SAE (Sequence-To-Sequence Auto-Encoder), which is composed of a sequence-to-sequence algorithm used in machine translation as well as an auto-encoder for effective compression of the input vector. Compared to the existing method, the method proposed in this paper uses fewer features of protein-compound complex and also show higher predictive accuracy.