Research Paper

Machine learning study of the extended drug–target interaction network informed by pain related voltage-gated sodium channels

Chen, Long^a; Jiang, Jian^a,b; Dou, Bozheng^a; Feng, Hongsong^b; Liu, Jie^a; Zhu, Yueying^a; Zhang, Bengong^a; Zhou, Tianshou^c; Wei, Guo-Wei^b,d,e,*

Author Information

^aResearch Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, P R. China

^bDepartment of Mathematics, Michigan State University, East Lansing, MI, United States

^cKey Laboratory of Computational Mathematics, Guangdong Province, and School of Mathematics, Sun Yat-sen University, Guangzhou, P R. China

Departments of ^dElectrical and Computer Engineering and

^eBiochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States

^*Corresponding author. Address: Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States. Tel.: +01-517-353-4689. E-mail address: [email protected] (G. W. Wei).

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.painjournalonline.com).

PAIN 165(4):p 908-921, April 2024. | DOI: 10.1097/j.pain.0000000000003089

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Abstract

Pain is a significant global health issue, and the current treatment options for pain management have limitations in terms of effectiveness, side effects, and potential for addiction. There is a pressing need for improved pain treatments and the development of new drugs. Voltage-gated sodium channels, particularly Nav1.3, Nav1.7, Nav1.8, and Nav1.9, play a crucial role in neuronal excitability and are predominantly expressed in the peripheral nervous system. Targeting these channels may provide a means to treat pain while minimizing central and cardiac adverse effects. In this study, we construct protein–protein interaction (PPI) networks based on pain-related sodium channels and develop a corresponding drug–target interaction network to identify potential lead compounds for pain management. To ensure reliable machine learning predictions, we carefully select 111 inhibitor data sets from a pool of more than 1000 targets in the PPI network. We employ 3 distinct machine learning algorithms combined with advanced natural language processing (NLP)–based embeddings, specifically pretrained transformer and autoencoder representations. Through a systematic screening process, we evaluate the side effects and repurposing potential of more than 150,000 drug candidates targeting Nav1.7 and Nav1.8 sodium channels. In addition, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify leads with near-optimal characteristics. Our strategy provides an innovative platform for the pharmacological development of pain treatments, offering the potential for improved efficacy and reduced side effects.