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TIDAL: Topology-Inferred Drug Addiction Learning by Zailiang Zhu; Bozheng Dou; Yukang Cao; Jian Jiang; Yueying Zhu; Dong Chen; Hongsong Feng; Jie Liu; Bengong Zhang; Tianshou Zhou; Guo-Wei Wei is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is TIDAL: Topology-Inferred Drug Addiction Learning about?

Drug addiction is a global public health crisis, and the design of antiaddiction drugs remains a major challenge due to intricate mechanisms. Since experimental drug screening and optimization are too time-consuming and expensive, there is urgent need to develop innovative artificial intelligence (AI) methods for addressing the challenge. We tackle this challenge by topology-inferred drug addiction learning (TIDAL) built from integrating multiscale topological Laplacians, deep bidirectional transformer, and ensemble-assisted neural networks (EANNs). Multiscale topological Laplacians are a novel class of algebraic topology tools that embed molecular topological invariants and algebraic invariants into its harmonic spectra and nonharmonic spectra, respectively. These invariants complement sequence information extracted from a bidirectional transformer. We validate the proposed TIDAL framework on 22 drug addiction related, 4 hERG, and 12 DAT data sets, which suggests that the proposed TIDAL is a state-of-the-art framework for the modeling and analysis of drug addiction data. We carry out cross-target analysis of the current drug addiction candidates to alert their side effects and ide

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Author

Zailiang Zhu; Bozheng Dou; Yukang Cao; Jian Jiang; Yueying Zhu; Dong Chen; Hongsong Feng; Jie Liu; Bengong Zhang; Tianshou Zhou; Guo-Wei Wei

Publisher

American Chemical Society (ACS)

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Physical Sciences)