TY - JOUR
T1 - AI-DrugNet
T2 - A network-based deep learning model for drug repurposing and combination therapy in neurological disorders
AU - Pan, Xingxin
AU - Yun, Jun
AU - Coban Akdemir, Zeynep H.
AU - Jiang, Xiaoqian
AU - Wu, Erxi
AU - Huang, Jason H.
AU - Sahni, Nidhi
AU - Yi, S. Stephen
N1 - Publisher Copyright:
© 2023
PY - 2023/1
Y1 - 2023/1
N2 - Discovering effective therapies is difficult for neurological and developmental disorders in that disease progression is often associated with a complex and interactive mechanism. Over the past few decades, few drugs have been identified for treating Alzheimer's disease (AD), especially for impacting the causes of cell death in AD. Although drug repurposing is gaining more success in developing therapeutic efficacy for complex diseases such as common cancer, the complications behind AD require further study. Here, we developed a novel prediction framework based on deep learning to identify potential repurposed drug therapies for AD, and more importantly, our framework is broadly applicable and may generalize to identifying potential drug combinations in other diseases. Our prediction framework is as follows: we first built a drug-target pair (DTP) network based on multiple drug features and target features, as well as the associations between DTP nodes where drug-target pairs are the DTP nodes and the associations between DTP nodes are represented as the edges in the AD disease network; furthermore, we incorporated the drug-target feature from the DTP network and the relationship information between drug-drug, target-target, drug-target within and outside of drug-target pairs, representing each drug-combination as a quartet to generate corresponding integrated features; finally, we developed an AI-based Drug discovery Network (AI-DrugNet), which exhibits robust predictive performance. The implementation of our network model help identify potential repurposed and combination drug options that may serve to treat AD and other diseases.
AB - Discovering effective therapies is difficult for neurological and developmental disorders in that disease progression is often associated with a complex and interactive mechanism. Over the past few decades, few drugs have been identified for treating Alzheimer's disease (AD), especially for impacting the causes of cell death in AD. Although drug repurposing is gaining more success in developing therapeutic efficacy for complex diseases such as common cancer, the complications behind AD require further study. Here, we developed a novel prediction framework based on deep learning to identify potential repurposed drug therapies for AD, and more importantly, our framework is broadly applicable and may generalize to identifying potential drug combinations in other diseases. Our prediction framework is as follows: we first built a drug-target pair (DTP) network based on multiple drug features and target features, as well as the associations between DTP nodes where drug-target pairs are the DTP nodes and the associations between DTP nodes are represented as the edges in the AD disease network; furthermore, we incorporated the drug-target feature from the DTP network and the relationship information between drug-drug, target-target, drug-target within and outside of drug-target pairs, representing each drug-combination as a quartet to generate corresponding integrated features; finally, we developed an AI-based Drug discovery Network (AI-DrugNet), which exhibits robust predictive performance. The implementation of our network model help identify potential repurposed and combination drug options that may serve to treat AD and other diseases.
KW - Deep learning
KW - Drug combination therapy
KW - Drug repurposing
KW - Network model
KW - Neurological and developmental disorders
UR - http://www.scopus.com/inward/record.url?scp=85148019103&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85148019103&partnerID=8YFLogxK
U2 - 10.1016/j.csbj.2023.02.004
DO - 10.1016/j.csbj.2023.02.004
M3 - Article
C2 - 36879885
AN - SCOPUS:85148019103
SN - 2001-0370
VL - 21
SP - 1533
EP - 1542
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
ER -