A Deep Learning-Based Chemical System for QSAR Prediction

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.

Authors

• ShanShan Hu
• Peng Chen
• Pengying Gu
• Bing Wang Alibaba Group

Keywords

• Chemicals
• Predictive models
• Inhibitors
• Biological system modeling
• Drugs
• Machine learning
• Compounds
• Quantitative Structure–activity Relationship
• Neural Network
• Drug Discovery
• Deep Learning
• Convolutional Neural Network
• Convolutional Neural Network Architecture
• Latent Features
• Chemical Molecules
• Promising Lead Compound
• Encoder-decoder Model
• Rise Of Deep Learning
• Prediction Model
• Active Compounds
• Random Forest
• Bioassay
• Convolutional Layers
• Active Molecules
• F1 Score
• Long Short-term Memory
• Long Short-term Memory Unit
• Gated Recurrent Unit
• Quantitative Structure–activity Relationship Models
• Original Vector
• Molecular Descriptors
• Inactive Molecules
• Inactive Compounds
• Encoder Network
• Attention Mechanism
• QSAR
• CNN
• encoder-decoder
• active molecule
• Computational Biology
• Models, Chemical
• Pharmaceutical Preparations
• Quantitative Structure-Activity Relationship