DeepGraphh: AI-driven web service for graph-based quantitative structure–activity relationship analysis. Issue 5 (23rd July 2022)
- Record Type:
- Journal Article
- Title:
- DeepGraphh: AI-driven web service for graph-based quantitative structure–activity relationship analysis. Issue 5 (23rd July 2022)
- Main Title:
- DeepGraphh: AI-driven web service for graph-based quantitative structure–activity relationship analysis
- Authors:
- Gautam, Vishakha
Gupta, Rahul
Gupta, Deepti
Ruhela, Anubhav
Mittal, Aayushi
Mohanty, Sanjay Kumar
Arora, Sakshi
Gupta, Ria
Saini, Chandan
Sengupta, Debarka
Murugan, Natarajan Arul
Ahuja, Gaurav - Abstract:
- Abstract: Artificial intelligence (AI)-based computational techniques allow rapid exploration of the chemical space. However, representation of the compounds into computational-compatible and detailed features is one of the crucial steps for quantitative structure–activity relationship (QSAR) analysis. Recently, graph-based methods are emerging as a powerful alternative to chemistry-restricted fingerprints or descriptors for modeling. Although graph-based modeling offers multiple advantages, its implementation demands in-depth domain knowledge and programming skills. Here we introduce deepGraphh, an end-to-end web service featuring a conglomerate of established graph-based methods for model generation for classification or regression tasks. The graphical user interface of deepGraphh supports highly configurable parameter support for model parameter tuning, model generation, cross-validation and testing of the user-supplied query molecules. deepGraphh supports four widely adopted methods for QSAR analysis, namely, graph convolution network, graph attention network, directed acyclic graph and Attentive FP. Comparative analysis revealed that deepGraphh supported methods are comparable to the descriptors-based machine learning techniques. Finally, we used deepGraphh models to predict the blood–brain barrier permeability of human and microbiome-generated metabolites. In summary, deepGraphh offers a one-stop web service for graph-based methods for chemoinformatics.
- Is Part Of:
- Briefings in bioinformatics. Volume 23:Issue 5(2022)
- Journal:
- Briefings in bioinformatics
- Issue:
- Volume 23:Issue 5(2022)
- Issue Display:
- Volume 23, Issue 5 (2022)
- Year:
- 2022
- Volume:
- 23
- Issue:
- 5
- Issue Sort Value:
- 2022-0023-0005-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-07-23
- Subjects:
- GNN -- DAG -- deep learning -- chemoinformatics -- classification -- QSAR -- BBB prediction
Genetics -- Data processing -- Periodicals
Molecular biology -- Data processing -- Periodicals
Genomes -- Data processing -- Periodicals
572.80285 - Journal URLs:
- http://bib.oxfordjournals.org ↗
http://www.oxfordjournals.org/content?genre=journal&issn=1477-4054 ↗
http://ukcatalogue.oup.com/ ↗
http://firstsearch.oclc.org ↗ - DOI:
- 10.1093/bib/bbac288 ↗
- Languages:
- English
- ISSNs:
- 1467-5463
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 2283.958363
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23923.xml