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In this study, we propose the convolutional recurrent neural network andtransfer learning (CRNNTL) for QSAR modelling. The method was inspired by theapplications of polyphonic sound detection and electrocardiogramclassification. Our strategy takes advantages of both convolutional andrecurrent neural networks for feature extraction, as well as the dataaugmentation method. Herein, CRNNTL is evaluated on 20 benchmark datasets incomparison with baseline methods. In addition, one isomers based dataset isused to elucidate its ability for both local and global feature extraction.Then, knowledge transfer performance of CRNNTL is tested, especially for smallbiological activity datasets. Finally, different latent representations fromother type of AEs were used for versatility study of our model. The resultsshow the effectiveness of CRNNTL using different latent representation.Moreover, efficient knowledge transfer is achieved to overcome data scarcityconsidering binding site similarity between different targets.

CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modelling