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Modeling DTA by Combining Multiple-Instance Learning with a Private-Public Mechanism
Version 1
: Received: 17 August 2022 / Approved: 18 August 2022 / Online: 18 August 2022 (03:58:34 CEST)
A peer-reviewed article of this Preprint also exists.
Wang, C.; Chen, Y.; Zhao, L.; Wang, J.; Wen, N. Modeling DTA by Combining Multiple-Instance Learning with a Private-Public Mechanism. Int. J. Mol. Sci. 2022, 23, 11136. Wang, C.; Chen, Y.; Zhao, L.; Wang, J.; Wen, N. Modeling DTA by Combining Multiple-Instance Learning with a Private-Public Mechanism. Int. J. Mol. Sci. 2022, 23, 11136.
Abstract
The prediction of drug-target interactions plays a fundamental role in facilitating drug discovery, where the goal is to find prospective drug candidates. With the increase in the number of drug-protein interactions, machine learning techniques, especially deep learning methods, have become applicable for drug-target interaction discovery because they significantly reduce the required experimental workload. In this paper, we present a spontaneous formulation of the drug-target interaction prediction problem as an instance of multi-instance learning. We address the problem in three stages, first organizing given drug and target sequences into instances via a private-public mechanism, then identifying the predicted scores of all instances in the same bag, and finally combining all the predicted scores as the output prediction. A comprehensive evaluation demonstrates that the proposed method outperforms other state-of-the-art methods on three benchmark datasets.
Keywords
Drug-Target Binding Affinity; Multi-Instance Learning; Transformer
Subject
Computer Science and Mathematics, Information Systems
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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