NeuroCADR: Drug Repurposing to Reveal Novel Anti-Epileptic Drug Candidates Through an Integrated Computational Approach

• URL: http://arxiv.org/abs/2309.13047v1
• Date: Mon, 4 Sep 2023 03:21:43 GMT
• Title: NeuroCADR: Drug Repurposing to Reveal Novel Anti-Epileptic Drug Candidates Through an Integrated Computational Approach
• Authors: Srilekha Mamidala
• Abstract summary: Drug repurposing is an emerging approach for drug discovery involving the reassignment of existing drugs for novel purposes. A proposed algorithm is NeuroCADR, a novel system for drug repurposing via a multi-pronged approach consisting of k-nearest neighbor algorithms (KNN), random forest classification, and decision trees. Data was sourced from several databases consisting of interactions between diseases, symptoms, genes, and affiliated drug molecules, which were then compiled into datasets expressed in binary. NeuroCADR identified novel drug candidates for epilepsy that can be further approved through clinical trials.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Drug repurposing is an emerging approach for drug discovery involving the reassignment of existing drugs for novel purposes. An alternative to the traditional de novo process of drug development, repurposed drugs are faster, cheaper, and less failure prone than drugs developed from traditional methods. Recently, drug repurposing has been performed in silico, in which databases of drugs and chemical information are used to determine interactions between target proteins and drug molecules to identify potential drug candidates. A proposed algorithm is NeuroCADR, a novel system for drug repurposing via a multi-pronged approach consisting of k-nearest neighbor algorithms (KNN), random forest classification, and decision trees. Data was sourced from several databases consisting of interactions between diseases, symptoms, genes, and affiliated drug molecules, which were then compiled into datasets expressed in binary. The proposed method displayed a high level of accuracy, outperforming nearly all in silico approaches. NeuroCADR was performed on epilepsy, a condition characterized by seizures, periods of time with bursts of uncontrolled electrical activity in brain cells. Existing drugs for epilepsy can be ineffective and expensive, revealing a need for new antiepileptic drugs. NeuroCADR identified novel drug candidates for epilepsy that can be further approved through clinical trials. The algorithm has the potential to determine possible drug combinations to prescribe a patient based on a patient's prior medical history. This project examines NeuroCADR, a novel approach to computational drug repurposing capable of revealing potential drug candidates in neurological diseases such as epilepsy.

Related papers

• DrugAgent: Explainable Drug Repurposing Agent with Large Language Model-based Reasoning [10.528489471229946]
  We propose a multi-agent framework to enhance the drug repurposing process using state-of-the-art machine learning techniques and knowledge integration. Our framework comprises several specialized agents: an AI Agent trains robust drug-target interaction (DTI) models; a Knowledge Graph Agent utilizes the drug-gene interaction database (DGIdb) to systematically extract DTIs. By integrating outputs from these agents, our system effectively harnesses diverse data sources, including external databases, to propose viable repurposing candidates.
  arXiv  Detail & Related papers  (2024-08-23T21:24:59Z)
• DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing [4.969453745531116]
  DrugCLIP is a contrastive learning method to learn drug and disease's interaction without negative labels. We have curated a drug repurposing dataset based on real-world clinical trial records.
  arXiv  Detail & Related papers  (2024-07-02T13:41:59Z)
• Drug Repurposing for Parkinson's Disease Using Random Walk With Restart Algorithm and the Parkinson's Disease Ontology Database [0.0]
  We design a novel computational platform that integrates gene expression data, biological networks, and the PDOD database to identify possible drug-repositioning agents for Parkinson's therapy. We propose gene expression analysis, network prioritization, and drug target data analysis to arrive at a comprehensive evaluation of drug repurposing chances.
  arXiv  Detail & Related papers  (2024-04-11T20:11:25Z)
• Learning to Describe for Predicting Zero-shot Drug-Drug Interactions [54.172575323610175]
  Adverse drug-drug interactions can compromise the effectiveness of concurrent drug administration. Traditional computational methods for DDI prediction may fail to capture interactions for new drugs due to the lack of knowledge. We propose TextDDI with a language model-based DDI predictor and a reinforcement learning(RL)-based information selector.
  arXiv  Detail & Related papers  (2024-03-13T09:42:46Z)
• 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)
• Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening [38.94493676651818]
  We propose a zero-shot learning solution for the. task in preclinical drug screening. Specifically, we propose a Multi-branch Multi-Source Domain Adaptation Test Enhancement Plug-in, called MSDA.
  arXiv  Detail & Related papers  (2023-10-05T05:55:41Z)
• Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy [63.135687276599114]
  Some polypharmacies, deemed inappropriate, may be associated with adverse health outcomes such as death or hospitalization. We propose the OptimNeuralTS strategy to efficiently mine claims datasets and build a predictive model of the association between drug combinations and health outcomes. Our method can detect up to 72% of PIPs while maintaining an average precision score of 99% using 30 000 time steps.
  arXiv  Detail & Related papers  (2022-12-10T03:43:23Z)
• Knowledge-Driven New Drug Recommendation [88.35607943144261]
  We develop a drug-dependent multi-phenotype few-shot learner to bridge the gap between existing and new drugs. EDGE eliminates the false-negative supervision signal using an external drug-disease knowledge base. Results show that EDGE achieves 7.3% improvement on the ROC-AUC score over the best baseline.
  arXiv  Detail & Related papers  (2022-10-11T16:07:52Z)
• Deep learning for drug repurposing: methods, databases, and applications [54.08583498324774]
  Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing.
  arXiv  Detail & Related papers  (2022-02-08T09:42:08Z)
• SafeDrug: Dual Molecular Graph Encoders for Safe Drug Recommendations [59.590084937600764]
  We propose a DDI-controllable drug recommendation model named SafeDrug to leverage drugs' molecule structures and model DDIs explicitly. On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19.43% and improves 2.88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches.
  arXiv  Detail & Related papers  (2021-05-05T00:20:48Z)
• When deep learning meets causal inference: a computational framework for drug repurposing from real-world data [12.68717103979673]
  Existing methods for drug repurposing may exist translational issues when applied to human beings. We present an efficient and easily-customized framework for generating and testing multiple candidates for drug repurposing. We demonstrate our framework in a case study of coronary artery disease (CAD) by evaluating the effect of 55 repurposing drug candidates on various disease outcomes.
  arXiv  Detail & Related papers  (2020-07-16T21:30:56Z)

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.