GraphADT: empowering interpretable predictions of acute dermal toxicity with multi-view graph pooling and structure remapping Article Swipe
Xinqian Ma
,
Xiangzheng Fu
,
Tao Wang
,
Linlin Zhuo
,
Quan Zou
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btae438
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btae438
Motivation Accurate prediction of acute dermal toxicity (ADT) is essential for the safe and effective development of contact drugs. Currently, graph neural networks, a form of deep learning technology, accurately model the structure of compound molecules, enhancing predictions of their ADT. However, many existing methods emphasize atom-level information transfer and overlook crucial data conveyed by molecular bonds and their interrelationships. Additionally, these methods often generate “equal” node representations across the entire graph, failing to accentuate “important” substructures like functional groups, pharmacophores, and toxicophores, thereby reducing interpretability. Results We introduce a novel model, GraphADT, utilizing structure remapping and multi-view graph pooling (MVPool) technologies to accurately predict compound ADT. Initially, our model applies structure remapping to better delineate bonds, transforming “bonds” into new nodes and “bond-atom-bond” interactions into new edges, thereby reconstructing the compound molecular graph. Subsequently, we use MVPool to amalgamate data from various perspectives, minimizing biases inherent to single-view analyses. Following this, the model generates a robust node ranking collaboratively, emphasizing critical nodes or substructures to enhance model interpretability. Lastly, we apply a graph comparison learning strategy to train both the original and structure remapped molecular graphs, deriving the final molecular representation. Experimental results on public datasets indicate that the GraphADT model outperforms existing state-of-the-art models. The GraphADT model has been demonstrated to effectively predict compound ADT, offering potential guidance for the development of contact drugs and related treatments. Availability and implementation Our code and data are accessible at: https://github.com/mxqmxqmxq/GraphADT.git.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btae438
- https://academic.oup.com/bioinformatics/article-pdf/40/7/btae438/58531307/btae438.pdf
- OA Status
- gold
- Cited By
- 12
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400362549
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4400362549Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/bioinformatics/btae438Digital Object Identifier
- Title
-
GraphADT: empowering interpretable predictions of acute dermal toxicity with multi-view graph pooling and structure remappingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-01Full publication date if available
- Authors
-
Xinqian Ma, Xiangzheng Fu, Tao Wang, Linlin Zhuo, Quan ZouList of authors in order
- Landing page
-
https://doi.org/10.1093/bioinformatics/btae438Publisher landing page
- PDF URL
-
https://academic.oup.com/bioinformatics/article-pdf/40/7/btae438/58531307/btae438.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/bioinformatics/article-pdf/40/7/btae438/58531307/btae438.pdfDirect OA link when available
- Concepts
-
Interpretability, Pooling, Computer science, Graph, Artificial intelligence, Theoretical computer science, Machine learning, Molecular graphTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 3Per-year citation counts (last 5 years)
- References (count)
-
22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4400362549 |
|---|---|
| doi | https://doi.org/10.1093/bioinformatics/btae438 |
| ids.doi | https://doi.org/10.1093/bioinformatics/btae438 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/38967119 |
| ids.openalex | https://openalex.org/W4400362549 |
| fwci | 9.48162689 |
| mesh[0].qualifier_ui | Q000187 |
| mesh[0].descriptor_ui | D012867 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | drug effects |
| mesh[0].descriptor_name | Skin |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D006801 |
| mesh[1].is_major_topic | False |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Humans |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D000077321 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Deep Learning |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D016571 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Neural Networks, Computer |
| mesh[4].qualifier_ui | Q000187 |
| mesh[4].descriptor_ui | D012867 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | drug effects |
| mesh[4].descriptor_name | Skin |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D006801 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Humans |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D000077321 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Deep Learning |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D016571 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Neural Networks, Computer |
| mesh[8].qualifier_ui | Q000187 |
| mesh[8].descriptor_ui | D012867 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | drug effects |
| mesh[8].descriptor_name | Skin |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D006801 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Humans |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D000077321 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Deep Learning |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D016571 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Neural Networks, Computer |
| mesh[12].qualifier_ui | Q000187 |
| mesh[12].descriptor_ui | D012867 |
| mesh[12].is_major_topic | True |
| mesh[12].qualifier_name | drug effects |
| mesh[12].descriptor_name | Skin |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D006801 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Humans |
| mesh[14].qualifier_ui | |
| mesh[14].descriptor_ui | D000077321 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | |
| mesh[14].descriptor_name | Deep Learning |
| mesh[15].qualifier_ui | |
| mesh[15].descriptor_ui | D016571 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | |
| mesh[15].descriptor_name | Neural Networks, Computer |
| type | article |
| title | GraphADT: empowering interpretable predictions of acute dermal toxicity with multi-view graph pooling and structure remapping |
| awards[0].id | https://openalex.org/G552737822 |
| awards[0].funder_id | https://openalex.org/F4320321001 |
| awards[0].display_name | |
| awards[0].funder_award_id | 62372158 |
| awards[0].funder_display_name | National Natural Science Foundation of China |
| awards[1].id | https://openalex.org/G7676913961 |
| awards[1].funder_id | https://openalex.org/F4320321001 |
| awards[1].display_name | |
| awards[1].funder_award_id | 62302339 |
| awards[1].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 7 |
| biblio.volume | 40 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10211 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1703 |
| topics[0].subfield.display_name | Computational Theory and Mathematics |
| topics[0].display_name | Computational Drug Discovery Methods |
| topics[1].id | https://openalex.org/T12023 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.987500011920929 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2736 |
| topics[1].subfield.display_name | Pharmacology |
| topics[1].display_name | Cholinesterase and Neurodegenerative Diseases |
| topics[2].id | https://openalex.org/T10887 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9222999811172485 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1312 |
| topics[2].subfield.display_name | Molecular Biology |
| topics[2].display_name | Bioinformatics and Genomic Networks |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| is_xpac | False |
| apc_list.value | 3618 |
| apc_list.currency | USD |
| apc_list.value_usd | 3618 |
| apc_paid.value | 3618 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 3618 |
| concepts[0].id | https://openalex.org/C2781067378 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9187246561050415 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q17027399 |
| concepts[0].display_name | Interpretability |
| concepts[1].id | https://openalex.org/C70437156 |
| concepts[1].level | 2 |
| concepts[1].score | 0.736932098865509 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q7228652 |
| concepts[1].display_name | Pooling |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.7354828119277954 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C132525143 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5389043092727661 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q141488 |
| concepts[3].display_name | Graph |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5015237331390381 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C80444323 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4980790615081787 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[5].display_name | Theoretical computer science |
| concepts[6].id | https://openalex.org/C119857082 |
| concepts[6].level | 1 |
| concepts[6].score | 0.46720096468925476 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[6].display_name | Machine learning |
| concepts[7].id | https://openalex.org/C2780022179 |
| concepts[7].level | 3 |
| concepts[7].score | 0.4224660098552704 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1986794 |
| concepts[7].display_name | Molecular graph |
| keywords[0].id | https://openalex.org/keywords/interpretability |
| keywords[0].score | 0.9187246561050415 |
| keywords[0].display_name | Interpretability |
| keywords[1].id | https://openalex.org/keywords/pooling |
| keywords[1].score | 0.736932098865509 |
| keywords[1].display_name | Pooling |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.7354828119277954 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/graph |
| keywords[3].score | 0.5389043092727661 |
| keywords[3].display_name | Graph |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5015237331390381 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[5].score | 0.4980790615081787 |
| keywords[5].display_name | Theoretical computer science |
| keywords[6].id | https://openalex.org/keywords/machine-learning |
| keywords[6].score | 0.46720096468925476 |
| keywords[6].display_name | Machine learning |
| keywords[7].id | https://openalex.org/keywords/molecular-graph |
| keywords[7].score | 0.4224660098552704 |
| keywords[7].display_name | Molecular graph |
| language | en |
| locations[0].id | doi:10.1093/bioinformatics/btae438 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S52395412 |
| locations[0].source.issn | 1367-4803, 1367-4811 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1367-4803 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Bioinformatics |
| locations[0].source.host_organization | https://openalex.org/P4310311648 |
| locations[0].source.host_organization_name | Oxford University Press |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| locations[0].source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://academic.oup.com/bioinformatics/article-pdf/40/7/btae438/58531307/btae438.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Bioinformatics |
| locations[0].landing_page_url | https://doi.org/10.1093/bioinformatics/btae438 |
| locations[1].id | pmid:38967119 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Bioinformatics (Oxford, England) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/38967119 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:11245318 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | other-oa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/other-oa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Bioinformatics |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11245318 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5037445454 |
| authorships[0].author.orcid | https://orcid.org/0009-0008-3798-4628 |
| authorships[0].author.display_name | Xinqian Ma |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I146620803, https://openalex.org/I4400573270 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Data Science and Artificial Intelligence, Wenzhou University of Technology , Wenzhou 325027, China |
| authorships[0].institutions[0].id | https://openalex.org/I4400573270 |
| authorships[0].institutions[0].ror | https://ror.org/03dd7qj98 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4400573270 |
| authorships[0].institutions[0].country_code | |
| authorships[0].institutions[0].display_name | Wenzhou University of Technology |
| authorships[0].institutions[1].id | https://openalex.org/I146620803 |
| authorships[0].institutions[1].ror | https://ror.org/020hxh324 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I146620803 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | Wenzhou University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Xinqian Ma |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Data Science and Artificial Intelligence, Wenzhou University of Technology , Wenzhou 325027, China |
| authorships[1].author.id | https://openalex.org/A5044283271 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6840-2573 |
| authorships[1].author.display_name | Xiangzheng Fu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I16609230 |
| authorships[1].affiliations[0].raw_affiliation_string | College of Computer Science and Electronic Engineering, Hunan University , Changsha 410012, China |
| authorships[1].institutions[0].id | https://openalex.org/I16609230 |
| authorships[1].institutions[0].ror | https://ror.org/05htk5m33 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I16609230 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Hunan University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Xiangzheng Fu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | College of Computer Science and Electronic Engineering, Hunan University , Changsha 410012, China |
| authorships[2].author.id | https://openalex.org/A5100453453 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-0202-0174 |
| authorships[2].author.display_name | Tao Wang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I146620803, https://openalex.org/I4400573270 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Data Science and Artificial Intelligence, Wenzhou University of Technology , Wenzhou 325027, China |
| authorships[2].institutions[0].id | https://openalex.org/I4400573270 |
| authorships[2].institutions[0].ror | https://ror.org/03dd7qj98 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I4400573270 |
| authorships[2].institutions[0].country_code | |
| authorships[2].institutions[0].display_name | Wenzhou University of Technology |
| authorships[2].institutions[1].id | https://openalex.org/I146620803 |
| authorships[2].institutions[1].ror | https://ror.org/020hxh324 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I146620803 |
| authorships[2].institutions[1].country_code | CN |
| authorships[2].institutions[1].display_name | Wenzhou University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Tao Wang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Data Science and Artificial Intelligence, Wenzhou University of Technology , Wenzhou 325027, China |
| authorships[3].author.id | https://openalex.org/A5004683765 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6586-0533 |
| authorships[3].author.display_name | Linlin Zhuo |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I146620803, https://openalex.org/I4400573270 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Data Science and Artificial Intelligence, Wenzhou University of Technology , Wenzhou 325027, China |
| authorships[3].institutions[0].id | https://openalex.org/I4400573270 |
| authorships[3].institutions[0].ror | https://ror.org/03dd7qj98 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I4400573270 |
| authorships[3].institutions[0].country_code | |
| authorships[3].institutions[0].display_name | Wenzhou University of Technology |
| authorships[3].institutions[1].id | https://openalex.org/I146620803 |
| authorships[3].institutions[1].ror | https://ror.org/020hxh324 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I146620803 |
| authorships[3].institutions[1].country_code | CN |
| authorships[3].institutions[1].display_name | Wenzhou University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Linlin Zhuo |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Data Science and Artificial Intelligence, Wenzhou University of Technology , Wenzhou 325027, China |
| authorships[4].author.id | https://openalex.org/A5017426085 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6406-1142 |
| authorships[4].author.display_name | Quan Zou |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I150229711 |
| authorships[4].affiliations[0].raw_affiliation_string | Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu 611730, China |
| authorships[4].institutions[0].id | https://openalex.org/I150229711 |
| authorships[4].institutions[0].ror | https://ror.org/04qr3zq92 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I150229711 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | University of Electronic Science and Technology of China |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Quan Zou |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu 611730, China |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://academic.oup.com/bioinformatics/article-pdf/40/7/btae438/58531307/btae438.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | GraphADT: empowering interpretable predictions of acute dermal toxicity with multi-view graph pooling and structure remapping |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10211 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1703 |
| primary_topic.subfield.display_name | Computational Theory and Mathematics |
| primary_topic.display_name | Computational Drug Discovery Methods |
| related_works | https://openalex.org/W2905433371, https://openalex.org/W2888392564, https://openalex.org/W4310278675, https://openalex.org/W4388422664, https://openalex.org/W4390569940, https://openalex.org/W3015684221, https://openalex.org/W4321793135, https://openalex.org/W4288804896, https://openalex.org/W2013842271, https://openalex.org/W2808877228 |
| cited_by_count | 12 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 9 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 3 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1093/bioinformatics/btae438 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S52395412 |
| best_oa_location.source.issn | 1367-4803, 1367-4811 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1367-4803 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Bioinformatics |
| best_oa_location.source.host_organization | https://openalex.org/P4310311648 |
| best_oa_location.source.host_organization_name | Oxford University Press |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| best_oa_location.source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://academic.oup.com/bioinformatics/article-pdf/40/7/btae438/58531307/btae438.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Bioinformatics |
| best_oa_location.landing_page_url | https://doi.org/10.1093/bioinformatics/btae438 |
| primary_location.id | doi:10.1093/bioinformatics/btae438 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S52395412 |
| primary_location.source.issn | 1367-4803, 1367-4811 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1367-4803 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Bioinformatics |
| primary_location.source.host_organization | https://openalex.org/P4310311648 |
| primary_location.source.host_organization_name | Oxford University Press |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| primary_location.source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://academic.oup.com/bioinformatics/article-pdf/40/7/btae438/58531307/btae438.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Bioinformatics |
| primary_location.landing_page_url | https://doi.org/10.1093/bioinformatics/btae438 |
| publication_date | 2024-07-01 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4226086528, https://openalex.org/W4376134822, https://openalex.org/W6849280526, https://openalex.org/W6811170508, https://openalex.org/W4385269452, https://openalex.org/W4294286981, https://openalex.org/W4391996674, https://openalex.org/W2822962393, https://openalex.org/W4394858080, https://openalex.org/W4293581802, https://openalex.org/W3085084733, https://openalex.org/W4387882024, https://openalex.org/W4391850848, https://openalex.org/W3120534749, https://openalex.org/W4394749617, https://openalex.org/W4324344331, https://openalex.org/W2754630916, https://openalex.org/W3110901318, https://openalex.org/W1774848501, https://openalex.org/W4390563851, https://openalex.org/W4386587901, https://openalex.org/W4226457859 |
| referenced_works_count | 22 |
| abstract_inverted_index.a | 24, 90, 156, 173 |
| abstract_inverted_index.We | 88 |
| abstract_inverted_index.by | 55 |
| abstract_inverted_index.is | 9 |
| abstract_inverted_index.of | 4, 17, 26, 34, 39, 224 |
| abstract_inverted_index.on | 195 |
| abstract_inverted_index.or | 164 |
| abstract_inverted_index.to | 74, 103, 114, 139, 148, 166, 178, 213 |
| abstract_inverted_index.we | 136, 171 |
| abstract_inverted_index.Our | 233 |
| abstract_inverted_index.The | 207 |
| abstract_inverted_index.and | 14, 50, 58, 82, 97, 123, 183, 227, 231, 235 |
| abstract_inverted_index.are | 237 |
| abstract_inverted_index.at: | 239 |
| abstract_inverted_index.for | 11, 221 |
| abstract_inverted_index.has | 210 |
| abstract_inverted_index.new | 121, 127 |
| abstract_inverted_index.our | 109 |
| abstract_inverted_index.the | 12, 32, 70, 131, 153, 181, 189, 200, 222 |
| abstract_inverted_index.use | 137 |
| abstract_inverted_index.ADT, | 217 |
| abstract_inverted_index.ADT. | 41, 107 |
| abstract_inverted_index.been | 211 |
| abstract_inverted_index.both | 180 |
| abstract_inverted_index.code | 234 |
| abstract_inverted_index.data | 53, 141, 236 |
| abstract_inverted_index.deep | 27 |
| abstract_inverted_index.form | 25 |
| abstract_inverted_index.from | 142 |
| abstract_inverted_index.into | 120, 126 |
| abstract_inverted_index.like | 78 |
| abstract_inverted_index.many | 43 |
| abstract_inverted_index.node | 67, 158 |
| abstract_inverted_index.safe | 13 |
| abstract_inverted_index.that | 199 |
| abstract_inverted_index.(ADT) | 8 |
| abstract_inverted_index.acute | 5 |
| abstract_inverted_index.apply | 172 |
| abstract_inverted_index.bonds | 57 |
| abstract_inverted_index.drugs | 226 |
| abstract_inverted_index.final | 190 |
| abstract_inverted_index.graph | 21, 99, 174 |
| abstract_inverted_index.model | 31, 110, 154, 168, 202, 209 |
| abstract_inverted_index.nodes | 122, 163 |
| abstract_inverted_index.novel | 91 |
| abstract_inverted_index.often | 64 |
| abstract_inverted_index.their | 40, 59 |
| abstract_inverted_index.these | 62 |
| abstract_inverted_index.this, | 152 |
| abstract_inverted_index.train | 179 |
| abstract_inverted_index.MVPool | 138 |
| abstract_inverted_index.across | 69 |
| abstract_inverted_index.better | 115 |
| abstract_inverted_index.biases | 146 |
| abstract_inverted_index.bonds, | 117 |
| abstract_inverted_index.dermal | 6 |
| abstract_inverted_index.drugs. | 19 |
| abstract_inverted_index.edges, | 128 |
| abstract_inverted_index.entire | 71 |
| abstract_inverted_index.graph, | 72 |
| abstract_inverted_index.graph. | 134 |
| abstract_inverted_index.model, | 92 |
| abstract_inverted_index.neural | 22 |
| abstract_inverted_index.public | 196 |
| abstract_inverted_index.robust | 157 |
| abstract_inverted_index.Lastly, | 170 |
| abstract_inverted_index.Results | 87 |
| abstract_inverted_index.applies | 111 |
| abstract_inverted_index.contact | 18, 225 |
| abstract_inverted_index.crucial | 52 |
| abstract_inverted_index.enhance | 167 |
| abstract_inverted_index.failing | 73 |
| abstract_inverted_index.graphs, | 187 |
| abstract_inverted_index.groups, | 80 |
| abstract_inverted_index.methods | 45, 63 |
| abstract_inverted_index.models. | 206 |
| abstract_inverted_index.pooling | 100 |
| abstract_inverted_index.predict | 105, 215 |
| abstract_inverted_index.ranking | 159 |
| abstract_inverted_index.related | 228 |
| abstract_inverted_index.results | 194 |
| abstract_inverted_index.thereby | 84, 129 |
| abstract_inverted_index.various | 143 |
| abstract_inverted_index.(MVPool) | 101 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Accurate | 2 |
| abstract_inverted_index.GraphADT | 201, 208 |
| abstract_inverted_index.However, | 42 |
| abstract_inverted_index.compound | 35, 106, 132, 216 |
| abstract_inverted_index.conveyed | 54 |
| abstract_inverted_index.critical | 162 |
| abstract_inverted_index.datasets | 197 |
| abstract_inverted_index.deriving | 188 |
| abstract_inverted_index.existing | 44, 204 |
| abstract_inverted_index.generate | 65 |
| abstract_inverted_index.guidance | 220 |
| abstract_inverted_index.indicate | 198 |
| abstract_inverted_index.inherent | 147 |
| abstract_inverted_index.learning | 28, 176 |
| abstract_inverted_index.offering | 218 |
| abstract_inverted_index.original | 182 |
| abstract_inverted_index.overlook | 51 |
| abstract_inverted_index.reducing | 85 |
| abstract_inverted_index.remapped | 185 |
| abstract_inverted_index.strategy | 177 |
| abstract_inverted_index.toxicity | 7 |
| abstract_inverted_index.transfer | 49 |
| abstract_inverted_index.Following | 151 |
| abstract_inverted_index.GraphADT, | 93 |
| abstract_inverted_index.analyses. | 150 |
| abstract_inverted_index.delineate | 116 |
| abstract_inverted_index.effective | 15 |
| abstract_inverted_index.emphasize | 46 |
| abstract_inverted_index.enhancing | 37 |
| abstract_inverted_index.essential | 10 |
| abstract_inverted_index.generates | 155 |
| abstract_inverted_index.introduce | 89 |
| abstract_inverted_index.molecular | 56, 133, 186, 191 |
| abstract_inverted_index.networks, | 23 |
| abstract_inverted_index.potential | 219 |
| abstract_inverted_index.remapping | 96, 113 |
| abstract_inverted_index.structure | 33, 95, 112, 184 |
| abstract_inverted_index.utilizing | 94 |
| abstract_inverted_index.Currently, | 20 |
| abstract_inverted_index.Initially, | 108 |
| abstract_inverted_index.Motivation | 1 |
| abstract_inverted_index.accentuate | 75 |
| abstract_inverted_index.accessible | 238 |
| abstract_inverted_index.accurately | 30, 104 |
| abstract_inverted_index.amalgamate | 140 |
| abstract_inverted_index.atom-level | 47 |
| abstract_inverted_index.comparison | 175 |
| abstract_inverted_index.functional | 79 |
| abstract_inverted_index.minimizing | 145 |
| abstract_inverted_index.molecules, | 36 |
| abstract_inverted_index.multi-view | 98 |
| abstract_inverted_index.prediction | 3 |
| abstract_inverted_index.development | 16, 223 |
| abstract_inverted_index.effectively | 214 |
| abstract_inverted_index.emphasizing | 161 |
| abstract_inverted_index.information | 48 |
| abstract_inverted_index.outperforms | 203 |
| abstract_inverted_index.predictions | 38 |
| abstract_inverted_index.single-view | 149 |
| abstract_inverted_index.technology, | 29 |
| abstract_inverted_index.treatments. | 229 |
| abstract_inverted_index.“bonds” | 119 |
| abstract_inverted_index.“equal” | 66 |
| abstract_inverted_index.Availability | 230 |
| abstract_inverted_index.Experimental | 193 |
| abstract_inverted_index.demonstrated | 212 |
| abstract_inverted_index.interactions | 125 |
| abstract_inverted_index.technologies | 102 |
| abstract_inverted_index.transforming | 118 |
| abstract_inverted_index.Additionally, | 61 |
| abstract_inverted_index.Subsequently, | 135 |
| abstract_inverted_index.perspectives, | 144 |
| abstract_inverted_index.substructures | 77, 165 |
| abstract_inverted_index.toxicophores, | 83 |
| abstract_inverted_index.implementation | 232 |
| abstract_inverted_index.reconstructing | 130 |
| abstract_inverted_index.pharmacophores, | 81 |
| abstract_inverted_index.representation. | 192 |
| abstract_inverted_index.representations | 68 |
| abstract_inverted_index.“important” | 76 |
| abstract_inverted_index.collaboratively, | 160 |
| abstract_inverted_index.state-of-the-art | 205 |
| abstract_inverted_index.interpretability. | 86, 169 |
| abstract_inverted_index.interrelationships. | 60 |
| abstract_inverted_index.“bond-atom-bond” | 124 |
| abstract_inverted_index.https://github.com/mxqmxqmxq/GraphADT.git. | 240 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.96684721 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |