A Deep Learning-Based Chemical System for QSAR Prediction.

IEEE Journal of Biomedical and Health Informatics
ShanShan HuBing Wang

Abstract

Research on quantitative structure-activity relationships (QSAR) provides an effective approach to determine new hits and promising lead compounds during drug discovery. In the past decades, various works have gained good performance for QSAR with the development of machine learning. The rise of deep learning, along with massive accessible chemical databases, made improvement on the QSAR performance. This article proposes a novel deep-learning-based method to implement QSAR prediction by the concatenation of end-to-end encoder-decoder model and convolutional neural network (CNN) architecture. The encoder-decoder model is mainly used to generate fixed-size latent features to represent chemical molecules; while these features are then input into CNN framework to train a robust and stable model and finally to predict active chemicals. Two models with different schemes are 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 in successful identification of chemical molecule whether it is active.

Citations

Aug 28, 2020·Frontiers in Bioengineering and Biotechnology·Ying LiangYinglong Wang
Jul 23, 2020·Frontiers in Bioengineering and Biotechnology·Mengran ZhouWenhao Lai
Jul 17, 2020·Frontiers in Bioengineering and Biotechnology·Chen ZhuWei Huang
Mar 9, 2021·BioMed Research International·Ruixi LiFangxiang Wu
Mar 16, 2021·Computers in Biology and Medicine·Madhulata Kumari, Naidu Subbarao
May 26, 2021·Molecular Diversity·Joel Markus Vaz, S Balaji
Jul 3, 2021·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Sharna-Kay Daley, Geoffrey A Cordell

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