Document Type

Article

Publication Date

2021

DOI

10.1089/cmb.2020.0538

Publication Title

Journal of Computational Biology

Volume

28

Pages

14 pp.

Abstract

In pharmaceutical sciences, a crucial step of the drug discovery is the identification of drug–target interactions (DTIs). However, only a small portion of the DTIs have been experimentally validated. Moreover, it is an extremely laborious, expensive, and time-consuming procedure to capture new interactions between drugs and targets through traditional biochemical experiments. Therefore, designing computational methods for predicting potential interactions to guide the experimental verification is of practical significance, especially for de novo situation. In this article, we propose a new algorithm, namely Laplacian regularized Schatten p-norm minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for new targets where there are no known interactions. Specifically, we first take advantage of the drug and target similarity information to dynamically prefill the partial unknown interactions. Then based on the assumption that the interaction matrix is low-rank, we use Schatten p-norm minimization model combined with Laplacian regularization terms to improve prediction performance in the new drug/target cases. Finally, we numerically solve the LRSpNM model by an efficient alternating direction method of multipliers algorithm. We evaluate LRSpNM on five data sets and an extensive set of numerical experiments show that LRSpNM achieves better and more robust performance than five state-of-the-art DTIs prediction algorithms. In addition, we conduct two case studies for new drug and new target prediction, which illustrates that LRSpNM can successfully predict most of the experimental validated DTIs.

Comments

© Mary Ann Liebert, Inc.

Publisher's edition available at: https://doi.org/10.1089/cmb.2020.0538

Included after a one year embargo with the permission of the publisher.

Original Publication Citation

Wu, G., Yang, M., Li, Y., & Wang, J. (2021). De novo prediction of drug–target interactions using Laplacian regularized Schatten p-norm minimization. Journal of Computational Biology, Ahead of print https://doi.org/10.1089/cmb.2020.0538

Available for download on Friday, January 21, 2022

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