Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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.

Journal reference: Int. J. Mol. Sci. 2022, 23, 11136
DOI: 10.3390/ijms231911136

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

MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management

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