Search Results for author:
Found 9 papers, 7 papers with code
Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data
Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology, offering unparalleled insights into the intricate landscape of cellular diversity and gene expression dynamics.
ChatGPT in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug Development with Chatbots
This paper not only explores the integration of advanced AI in drug discovery but also reimagines the landscape by advocating for AI-powered chatbots as trailblazers in revolutionizing therapeutic innovation.
Machine Learning Study of the Extended Drug-target Interaction Network informed by Pain Related Voltage-Gated Sodium Channels
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.
Multi-objective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex
Opioid Use Disorder (OUD) has emerged as a significant global public health issue, with complex multifaceted conditions.
Machine-learning Repurposing of DrugBank Compounds for Opioid Use Disorder
Using these predictors, we systematically analyzed the binding affinities of DrugBank compounds on four opioid receptors.
Machine-learning Analysis of Opioid Use Disorder Informed by MOR, DOR, KOR, NOR and ZOR-Based Interactome Networks
Despite that a few pharmacological agents have been approved for OUD treatment, the efficacy of said agents for OUD requires further improvement in order to provide safer and more effective pharmacological and psychosocial treatments.
SVSBI: Sequence-based virtual screening of biomolecular interactions
Virtual screening (VS) is an essential technique for understanding biomolecular interactions, particularly, drug design and discovery.
Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models
We are particularly interested in the hERG-cardiotoxicity of compounds collected in the DrugBank database considering that many DrugBank compounds have been approved for therapeutic treatments or have high potential to become drugs.
Machine learning analysis of cocaine addiction informed by DAT, SERT, and NET-based interactome networks
Cocaine dependence is neurological and involves many interacting proteins in the interactome.
