Machine Learning Study of the Extended Drug-target Interaction Network
  informed by Pain Related Voltage-Gated Sodium Channels
Long Chen Jian Jiang Bozheng Dou Hongsong Feng Jie Liu Yueying Zhu Bengong Zhang Tianshou Zhou Guo-Wei Wei
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 (DTI) network to
identify potential lead compounds for pain management. To ensure reliable
machine learning predictions, we carefully select 111 inhibitor datasets from a
pool of over 1,000 targets in the PPI network. We employ three distinct machine
learning algorithms combined with advanced natural language processing
(NLP)-based embeddings, specifically pre-trained transformer and autoencoder
representations. Through a systematic screening process, we evaluate the side
effects and repurposing potential of over 150,000 drug candidates targeting
Nav1.7 and Nav1.8 sodium channels. Additionally, 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.