A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

• URL: http://arxiv.org/abs/2102.10062v1
• Date: Fri, 19 Feb 2021 17:49:38 GMT
• Title: A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective
• Authors: Stephen Bonner and Ian P Barrett and Cheng Ye and Rowan Swiers and Ola Engkvist and William Hamilton
• Abstract summary: We aim to help guide machine learning and knowledge graph practitioners who are interested in applying new techniques to the drug discovery field. We detail publicly available primary data sources containing information suitable for use in constructing various drug discovery focused knowledge graphs.
• Score: 4.746544835197422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Drug discovery and development is an extremely complex process, with high attrition contributing to the costs of delivering new medicines to patients. Recently, various machine learning approaches have been proposed and investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Among these techniques, it is especially those using Knowledge Graphs that are proving to have considerable promise across a range of tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritisation. In such a knowledge graph-based representation of drug discovery domains, crucial elements including genes, diseases and drugs are represented as entities or vertices, whilst relationships or edges between them indicate some level of interaction. For example, an edge between a disease and drug entity might represent a successful clinical trial, or an edge between two drug entities could indicate a potentially harmful interaction. In order to construct high-quality and ultimately informative knowledge graphs however, suitable data and information is of course required. In this review, we detail publicly available primary data sources containing information suitable for use in constructing various drug discovery focused knowledge graphs. We aim to help guide machine learning and knowledge graph practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. Overall we hope this review will help motivate more machine learning researchers to explore combining knowledge graphs and machine learning to help solve key and emerging questions in the drug discovery domain.

Related papers

• 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)
• Learning a Patent-Informed Biomedical Knowledge Graph Reveals Technological Potential of Drug Repositioning Candidates [6.268435617836703]
  This study presents a novel protocol to analyse various sources such as pharmaceutical patents and biomedical databases. We identify drug repositioning candidates with both technological potential and scientific evidence. Our case study on Alzheimer's disease demonstrates its efficacy and feasibility.
  arXiv  Detail & Related papers  (2023-09-04T02:30:19Z)
• 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)
• Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey [6.288056740658763]
  Graph Machine Learning (GML) has gained considerable attention for its exceptional ability to model graph-structured biomedical data. Recent studies have proposed integrating external biomedical knowledge into the GML pipeline to realise more precise and interpretable drug discovery.
  arXiv  Detail & Related papers  (2023-02-16T12:38:01Z)
• 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)
• GraphIX: Graph-based In silico XAI(explainable artificial intelligence) for drug repositioning from biopharmaceutical network [1.4174475093445233]
  GraphIX is an explainable drug repositioning framework using biological networks. It can present to users new disease-drug associations and identify the protein important for understanding its pharmacological effects.
  arXiv  Detail & Related papers  (2022-12-21T06:17:45Z)
• A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs [2.4087148947930634]
  We develop a methodology to predict drug side-effects using Graph Neural Networks. We build a dataset from freely accessible and well established data sources. The results show that our method has an improved classification capability, under many parameters and metrics.
  arXiv  Detail & Related papers  (2022-11-30T10:12:41Z)
• 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)
• DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations [90.27736364704108]
  We present DrugOOD, a systematic OOD dataset curator and benchmark for AI-aided drug discovery. DrugOOD comes with an open-source Python package that fully automates benchmarking processes. We focus on one of the most crucial problems in AIDD: drug target binding affinity prediction.
  arXiv  Detail & Related papers  (2022-01-24T12:32:48Z)
• HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data [56.53715632642495]
  Clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment. In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions.
  arXiv  Detail & Related papers  (2021-02-08T15:09:07Z)

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.