An end-to-end deep-learning-based model for
To predict qsar by a CNN system ...
Research on quantitative structure-activity
relationships (QSAR) provides an effective ap proach to
accurately determine new hits and promising lead compounds
during drug discovery process. In the past decades, various
works have gained highlighted performance with the de
velopment of machine learning algorithms. The rise of deep
learning techniques, along with massive accessible chemical
databases, has improved the QSAR predictive performance.
This paper proposed a novel end-to-end deep-learning-based
model to implement QSAR prediction by the concatenation of
encoder-decoder model and convolutional neural network (CNN)
ar chitecture. The encoder-decoder model is mainly used to
generate fixed-size latent features to represent chemical
molecules; while CNN framework is used to train a robust and
stable model by these features to predict active chemicals.
Two different schemes were investigated to evaluate the
validity of our proposed model on the same data sets.
Experimental results showed that our proposed method
outperforms other state-of-the-art methods, which indicates
that our model can successfully identify a chemical molecule
whether it is active.
drug-target datasets and supplementary files:
supplementary (including enzymes,
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