Compound screening by in silico approaches has advantages in identifying high-activity leading compounds and can predict the safety of the drug. A key challenge is that the number of observations of drug activity and toxicity accumulation varies by target in different datasets, some of which are more understudied than others. Owing to an overall insufficiency and imbalance of drug data, it is hard to accurately predict drug activity and toxicity of multiple tasks by the existing models. To solve this problem, this paper proposed a two-stage transfer learning workflow to develop a novel prediction model, which can accurately predict drug activity and toxicity of the targets with insufficient observations. We built a balanced dataset based on the Tox21 dataset and developed a drug activity and toxicity prediction model based on Siamese networks and graph convolution to produce multitasking output. We also took advantage of transfer learning from data-rich targets to data-poor targets. We showed greater accuracy in predicting the activity and toxicity of compounds to targets with rich data and poor data. In Tox21, a relatively rich dataset, the prediction model accuracy for classification tasks was 0.877 AUROC. In the other five unbalanced datasets, we also found that transfer learning strategies brought the accuracy of models to a higher level in understudied targets. Our models can overcome the imbalance in target data and predict the compound activity and toxicity of understudied targets to help prioritize upcoming biological experiments.
Visual quality assessment is often used as a key performance indicator (KPI) to evaluate the performance of electronic devices. There exists a significant association between visual quality assessment and electronic devices. In this paper, we bring attention to alternative choices of perceptual loss function for end-to-end deep video coding (E2E-DVC), which can be used to reduce the amount of data generated by electronic sensors and other sources. Thus, we analyze the effects of different full-reference quality assessment (FR-QA) metrics on E2E-DVC. First, we select five optimization-suitable FR-QA metrics as perceptual objectives, which are differentiable and thus support back propagation, and use them to optimize an E2E-DVC model. Second, we analyze the rate–distortion (R-D) behaviors of an E2E-DVC model under different loss function optimizations. Third, we carry out subjective human perceptual tests on the reconstructed videos to show the performance of different FR-QA optimizations on subjective visual quality. This study reveals the effects of the competing FR-QA metrics on E2E-DVC and provides a guide for further future study on E2E-DVC in terms of perceptual loss function design.
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