Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction

• URL: http://arxiv.org/abs/2012.08194v2
• Date: Mon, 21 Dec 2020 14:47:48 GMT
• Title: Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction
• Authors: QHwan Kim, Joon-Hyuk Ko, Sunghoon Kim, Nojun Park, Wonho Jhe
• Abstract summary: Deep learning approaches can predict drug-protein interactions without trial-and-error by humans. We propose two methods to construct a deep learning framework that exhibits superior performance with a small labeled dataset.
• Score: 3.499870393443268
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The characterization of drug-protein interactions is crucial in the high-throughput screening for drug discovery. The deep learning-based approaches have attracted attention because they can predict drug-protein interactions without trial-and-error by humans. However, because data labeling requires significant resources, the available protein data size is relatively small, which consequently decreases model performance. Here we propose two methods to construct a deep learning framework that exhibits superior performance with a small labeled dataset. At first, we use transfer learning in encoding protein sequences with a pretrained model, which trains general sequence representations in an unsupervised manner. Second, we use a Bayesian neural network to make a robust model by estimating the data uncertainty. As a result, our model performs better than the previous baselines for predicting drug-protein interactions. We also show that the quantified uncertainty from the Bayesian inference is related to the confidence and can be used for screening DPI data points.

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