VDDB: a comprehensive resource and machine learning platform for
antiviral drug discovery
- URL: http://arxiv.org/abs/2209.13521v1
- Date: Sat, 17 Sep 2022 09:02:46 GMT
- Title: VDDB: a comprehensive resource and machine learning platform for
antiviral drug discovery
- Authors: Shunming Tao, Yihao Chen, Jingxing Wu, Duancheng Zhao, Hanxuan Cai,
Ling Wang
- Abstract summary: Virus infection is one of the major diseases that seriously threaten human health.
We presented an open-access antiviral drug resource and machine learning platform (VDDB)
VDDB highlights 848 clinical vaccines, 199 clinical antibodies, as well as over 710,000 small molecules targeting 39 medically important viruses including SARS-CoV-2.
- Score: 3.49680989341015
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Virus infection is one of the major diseases that seriously threaten human
health. To meet the growing demand for mining and sharing data resources
related to antiviral drugs and to accelerate the design and discovery of new
antiviral drugs, we presented an open-access antiviral drug resource and
machine learning platform (VDDB), which, to the best of our knowledge, is the
first comprehensive dedicated resource for experimentally verified potential
drugs/molecules based on manually curated data. Currently, VDDB highlights 848
clinical vaccines, 199 clinical antibodies, as well as over 710,000 small
molecules targeting 39 medically important viruses including SARS-CoV-2.
Furthermore, VDDB stores approximately 3 million records of pharmacological
data for these collected potential antiviral drugs/molecules, involving 314
cell infection-based phenotypic and 234 target-based genotypic assays. Based on
these annotated pharmacological data, VDDB allows users to browse, search and
download reliable information about these collects for various viruses of
interest. In particular, VDDB also integrates 57 cell infection- and 117
target-based associated high-accuracy machine learning models to support
various antivirals identification-related tasks, such as compound activity
prediction, virtual screening, drug repositioning and target fishing. VDDB is
freely accessible at http://vddb.idruglab.cn.
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) - A clustering and graph deep learning-based framework for COVID-19 drug
repurposing [0.3359875577705538]
This study presents a novel unsupervised machine learning framework that utilizes a graph-based autoencoder for multi-feature type clustering on heterogeneous drug data.
The dataset consists of 438 drugs, of which 224 are under clinical trials for COVID-19.
Our framework relies on reported drug data, including its pharmacological properties, chemical/physical properties, interaction with the host, and efficacy in different publicly available COVID-19 assays.
arXiv Detail & Related papers (2023-06-24T15:00:47Z) - 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) - COVID-Net Biochem: An Explainability-driven Framework to Building
Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19
Patients from Clinical and Biochemistry Data [66.43957431843324]
We introduce COVID-Net Biochem, a versatile and explainable framework for constructing machine learning models.
We apply this framework to predict COVID-19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization.
arXiv Detail & Related papers (2022-04-24T07:38:37Z) - 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) - 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) - Drug repurposing for COVID-19 using graph neural network and harmonizing
multiple evidence [9.472330151855111]
We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes.
We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records.
arXiv Detail & Related papers (2020-09-23T04:47:59Z) - Machine-Learning Driven Drug Repurposing for COVID-19 [0.47791962198275073]
We aim to discover the underlying associations between viral proteins and antiviral therapeutics by employing neural network models.
We trained ANN models with virus protein sequences as inputs and antiviral agents deemed safe-in-humans as outputs.
Using sequences for SARS-CoV-2 as inputs to the trained models produces tentative safe-in-human antiviral candidates for treating COVID-19.
arXiv Detail & Related papers (2020-06-25T21:18:53Z) - Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep
Learning [22.01390057543923]
There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic.
There are currently no proven effective medications against COVID-19.
This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19.
arXiv Detail & Related papers (2020-05-21T16:02:29Z) - Mapping the Landscape of Artificial Intelligence Applications against
COVID-19 [59.30734371401316]
COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization.
We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
arXiv Detail & Related papers (2020-03-25T12:30:33Z)
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.