Multiscale Topology in Interactomic Network: From Transcriptome to Antiaddiction Drug Repurposing

• URL: http://arxiv.org/abs/2312.01272v1
• Date: Sun, 3 Dec 2023 04:01:38 GMT
• Title: Multiscale Topology in Interactomic Network: From Transcriptome to Antiaddiction Drug Repurposing
• Authors: Hongyan Du, Guo-Wei Wei, Tingjun Hou
• Abstract summary: The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment.
• Score: 0.3683202928838613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery. We initiated our approach by conducting differential gene expression analysis on addiction-related transcriptomic data to identify key genes. We propose a novel topological differentiation to identify key genes from a protein-protein interaction (PPI) network derived from DEGs. This method utilizes persistent Laplacians to accurately single out pivotal nodes within the network, conducting this analysis in a multiscale manner to ensure high reliability. Through rigorous literature validation, pathway analysis, and data-availability scrutiny, we identified three pivotal molecular targets, mTOR, mGluR5, and NMDAR, for drug repurposing from DrugBank. We crafted machine learning models employing two natural language processing (NLP)-based embeddings and a traditional 2D fingerprint, which demonstrated robust predictive ability in gauging binding affinities of DrugBank compounds to selected targets. Furthermore, we elucidated the interactions of promising drugs with the targets and evaluated their drug-likeness. This study delineates a multi-faceted and comprehensive analytical framework, amalgamating bioinformatics, topological data analysis and machine learning, for drug repurposing in addiction treatment, setting the stage for subsequent experimental validation. The versatility of the methods we developed allows for applications across a range of diseases and transcriptomic datasets.

Related papers

• Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials [49.19897427783105]
  The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes.
  arXiv  Detail & Related papers  (2024-09-06T02:03:38Z)
• Enhancing Acute Kidney Injury Prediction through Integration of Drug Features in Intensive Care Units [0.0]
  The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs has yet to be explored in the critical care setting. This study proposes a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction.
  arXiv  Detail & Related papers  (2024-01-09T05:42:32Z)
• Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network [69.16939798838159]
  We propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.
  arXiv  Detail & Related papers  (2023-11-15T06:34:00Z)
• PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps [4.590060921188914]
  Key aspect of drug discovery involves identifying novel drug-target (DT) interactions. Protein-ligand interactions exhibit a continuum of binding strengths, known as binding affinity. We propose novel enhancements to enhance their performance.
  arXiv  Detail & Related papers  (2023-10-06T05:00:25Z)
• SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design [64.69434941796904]
  We propose a novel setting and models for in-context drug synergy learning. We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets. Our goal is to predict additional drug synergy relationships in that context.
  arXiv  Detail & Related papers  (2023-06-19T17:03:46Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction [127.43571146741984]
  Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery. wet experiments remain the most reliable method, but they are time-consuming and resource-intensive. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
  arXiv  Detail & Related papers  (2022-06-20T14:53:25Z)
• Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer [98.8319016075089]
  We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture. Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively. IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
  arXiv  Detail & Related papers  (2021-10-14T13:28:02Z)
• Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations [7.149873402253933]
  Microbes have become novel targets for the development of antibacterial agents. screening of microbe-drug associations can benefit greatly drug research and development. We propose a novel method, Graph2MDA, to predict microbe-drug associations.
  arXiv  Detail & Related papers  (2021-08-14T07:33:05Z)
• DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations [0.9854322576538699]
  computational screening has become an important way to prioritize drug combinations. DeepDDS was superior to competitive methods by more than 16% predictive precision.
  arXiv  Detail & Related papers  (2021-07-06T08:25:43Z)
• Interpretable Drug Synergy Prediction with Graph Neural Networks for Human-AI Collaboration in Healthcare [23.151336811933938]
  We propose a deep graph neural network, IDSP, to incorporate the gene-gene as well as gene-drug regulatory relationships in synergic drug combination predictions. IDSP automatically learns weights of edges based on the gene and drug node relations, by a multi-layer perceptron (MLP) and aggregates information in an inductive manner. We test IDWSP on signaling networks formulated by genes from 46 core cancer signaling pathways and drug combinations from NCI ALMANAC drug combination screening data.
  arXiv  Detail & Related papers  (2021-05-14T22:20:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.