Predicting PROTAC off-target effects via warhead involvement levels in drug–target interactions using graph attention neural networks Article Swipe
Ying Hu
,
Kieran Didi
,
Adam P. Cribbs
,
Jianfeng Sun
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.csbj.2025.10.028
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.csbj.2025.10.028
Proteolysis-targeting chimeras (PROTACs) represent an emerging modality for targeted protein degradation with broad therapeutic potential. However, the risk of off-target protein degradation remains a major concern in the development of PROTAC-based therapeutics. Here, we present SENTINEL, a graph-based deep learning framework that predicts the off-target propensity of PROTAC warheads based on their involvement levels in drug-target interactions as determined from established databases and the literature. By encoding warheads as molecular graphs using path-augmented graph transformer networks (PAGTNs), we show that graph attention-based neural networks (GATs) achieve accurate modelling of binding count-based off-target effects with an area under the ROC curve (AUC) of 0.9600 and an F1-score of 0.6983, outperforming classical machine learning algorithms such as random forests (AUC=0.840, F1-score=0.2778). SENTINEL provides a scalable strategy to prioritise lower-risk warheads in a low-data setting, supporting early-stage evaluation of PROTAC off-target risk. Results should be interpreted with the dataset size in mind and will benefit from larger external validation.
Related Topics
Concepts
No concepts available.
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.csbj.2025.10.028
- OA Status
- gold
- References
- 64
- OpenAlex ID
- https://openalex.org/W4415341770
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4415341770Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.csbj.2025.10.028Digital Object Identifier
- Title
-
Predicting PROTAC off-target effects via warhead involvement levels in drug–target interactions using graph attention neural networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-01Full publication date if available
- Authors
-
Ying Hu, Kieran Didi, Adam P. Cribbs, Jianfeng SunList of authors in order
- Landing page
-
https://doi.org/10.1016/j.csbj.2025.10.028Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.csbj.2025.10.028Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
64Number of works referenced by this work
Full payload
| id | https://openalex.org/W4415341770 |
|---|---|
| doi | https://doi.org/10.1016/j.csbj.2025.10.028 |
| ids.doi | https://doi.org/10.1016/j.csbj.2025.10.028 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/41245892 |
| ids.openalex | https://openalex.org/W4415341770 |
| fwci | 0.0 |
| type | article |
| title | Predicting PROTAC off-target effects via warhead involvement levels in drug–target interactions using graph attention neural networks |
| biblio.issue | |
| biblio.volume | 27 |
| biblio.last_page | 4644 |
| biblio.first_page | 4633 |
| topics[0].id | https://openalex.org/T12534 |
| topics[0].field.id | https://openalex.org/fields/13 |
| topics[0].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1312 |
| topics[0].subfield.display_name | Molecular Biology |
| topics[0].display_name | Protein Degradation and Inhibitors |
| topics[1].id | https://openalex.org/T11041 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9829999804496765 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1312 |
| topics[1].subfield.display_name | Molecular Biology |
| topics[1].display_name | Ubiquitin and proteasome pathways |
| topics[2].id | https://openalex.org/T10649 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9761999845504761 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2720 |
| topics[2].subfield.display_name | Hematology |
| topics[2].display_name | Multiple Myeloma Research and Treatments |
| is_xpac | False |
| apc_list.value | 2450 |
| apc_list.currency | EUR |
| apc_list.value_usd | 2642 |
| apc_paid.value | 2450 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2642 |
| language | en |
| locations[0].id | doi:10.1016/j.csbj.2025.10.028 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2764539591 |
| locations[0].source.issn | 2001-0370 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2001-0370 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Computational and Structural Biotechnology Journal |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Computational and Structural Biotechnology Journal |
| locations[0].landing_page_url | https://doi.org/10.1016/j.csbj.2025.10.028 |
| locations[1].id | pmid:41245892 |
| 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 | Computational and structural biotechnology journal |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/41245892 |
| locations[2].id | pmh:oai:europepmc.org:11421375 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400806 |
| 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 | Europe PMC (PubMed Central) |
| locations[2].source.host_organization | https://openalex.org/I1303153112 |
| locations[2].source.host_organization_name | European Bioinformatics Institute |
| locations[2].source.host_organization_lineage | https://openalex.org/I1303153112 |
| 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 | |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/12613028 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5100668236 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-3807-3649 |
| authorships[0].author.display_name | Ying Hu |
| authorships[0].countries | CN, HK |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I200769079 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Data Science and Technology, Hong Kong University of Science and Technology, Hong Kong, SAR 999077, China. |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I4210089756 |
| authorships[0].affiliations[1].raw_affiliation_string | Thrust of Bioscience and Biomedical Engineering, Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China. |
| authorships[0].institutions[0].id | https://openalex.org/I4210089756 |
| authorships[0].institutions[0].ror | https://ror.org/0072d4h61 |
| authorships[0].institutions[0].type | company |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210089756 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Bioscience (China) |
| authorships[0].institutions[1].id | https://openalex.org/I200769079 |
| authorships[0].institutions[1].ror | https://ror.org/00q4vv597 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I200769079 |
| authorships[0].institutions[1].country_code | HK |
| authorships[0].institutions[1].display_name | Hong Kong University of Science and Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yutong Hu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Data Science and Technology, Hong Kong University of Science and Technology, Hong Kong, SAR 999077, China., Thrust of Bioscience and Biomedical Engineering, Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China. |
| authorships[1].author.id | https://openalex.org/A5031230520 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6839-3320 |
| authorships[1].author.display_name | Kieran Didi |
| authorships[1].countries | GB |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I40120149 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Computer Science, University of Oxford, Oxford OX1 3QG, UK. |
| authorships[1].institutions[0].id | https://openalex.org/I40120149 |
| authorships[1].institutions[0].ror | https://ror.org/052gg0110 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I40120149 |
| authorships[1].institutions[0].country_code | GB |
| authorships[1].institutions[0].display_name | University of Oxford |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Kieran Didi |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computer Science, University of Oxford, Oxford OX1 3QG, UK. |
| authorships[2].author.id | https://openalex.org/A5045781099 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-5288-3077 |
| authorships[2].author.display_name | Adam P. Cribbs |
| authorships[2].countries | GB |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I40120149 |
| authorships[2].affiliations[0].raw_affiliation_string | Botnar Research Centre, University of Oxford, Oxford OX3 7LD, UK. |
| authorships[2].institutions[0].id | https://openalex.org/I40120149 |
| authorships[2].institutions[0].ror | https://ror.org/052gg0110 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I40120149 |
| authorships[2].institutions[0].country_code | GB |
| authorships[2].institutions[0].display_name | University of Oxford |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Adam P. Cribbs |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Botnar Research Centre, University of Oxford, Oxford OX3 7LD, UK. |
| authorships[3].author.id | https://openalex.org/A5078691732 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-1274-5080 |
| authorships[3].author.display_name | Jianfeng Sun |
| authorships[3].countries | CN, GB, HK |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I40120149 |
| authorships[3].affiliations[0].raw_affiliation_string | Botnar Research Centre, University of Oxford, Oxford OX3 7LD, UK. |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I200769079, https://openalex.org/I4210089756 |
| authorships[3].affiliations[1].raw_affiliation_string | Thrust of Bioscience and Biomedical Engineering, Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China. |
| authorships[3].institutions[0].id | https://openalex.org/I4210089756 |
| authorships[3].institutions[0].ror | https://ror.org/0072d4h61 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210089756 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Bioscience (China) |
| authorships[3].institutions[1].id | https://openalex.org/I40120149 |
| authorships[3].institutions[1].ror | https://ror.org/052gg0110 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I40120149 |
| authorships[3].institutions[1].country_code | GB |
| authorships[3].institutions[1].display_name | University of Oxford |
| authorships[3].institutions[2].id | https://openalex.org/I200769079 |
| authorships[3].institutions[2].ror | https://ror.org/00q4vv597 |
| authorships[3].institutions[2].type | education |
| authorships[3].institutions[2].lineage | https://openalex.org/I200769079 |
| authorships[3].institutions[2].country_code | HK |
| authorships[3].institutions[2].display_name | Hong Kong University of Science and Technology |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Jianfeng Sun |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Botnar Research Centre, University of Oxford, Oxford OX3 7LD, UK., Thrust of Bioscience and Biomedical Engineering, Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China. |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.csbj.2025.10.028 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-21T00:00:00 |
| display_name | Predicting PROTAC off-target effects via warhead involvement levels in drug–target interactions using graph attention neural networks |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12534 |
| primary_topic.field.id | https://openalex.org/fields/13 |
| primary_topic.field.display_name | Biochemistry, Genetics and Molecular Biology |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1312 |
| primary_topic.subfield.display_name | Molecular Biology |
| primary_topic.display_name | Protein Degradation and Inhibitors |
| cited_by_count | 0 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1016/j.csbj.2025.10.028 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764539591 |
| best_oa_location.source.issn | 2001-0370 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2001-0370 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Computational and Structural Biotechnology Journal |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Computational and Structural Biotechnology Journal |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.csbj.2025.10.028 |
| primary_location.id | doi:10.1016/j.csbj.2025.10.028 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2764539591 |
| primary_location.source.issn | 2001-0370 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2001-0370 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Computational and Structural Biotechnology Journal |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Computational and Structural Biotechnology Journal |
| primary_location.landing_page_url | https://doi.org/10.1016/j.csbj.2025.10.028 |
| publication_date | 2025-01-01 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2912114516, https://openalex.org/W2161531371, https://openalex.org/W4225687325, https://openalex.org/W2996380129, https://openalex.org/W2310322011, https://openalex.org/W3049656208, https://openalex.org/W4391994105, https://openalex.org/W4226188826, https://openalex.org/W3005962215, https://openalex.org/W2982554531, https://openalex.org/W2704517448, https://openalex.org/W2767856718, https://openalex.org/W2133739087, https://openalex.org/W4389894233, https://openalex.org/W3197771274, https://openalex.org/W4392157568, https://openalex.org/W4200613615, https://openalex.org/W1565539350, https://openalex.org/W4282935641, https://openalex.org/W3207463170, https://openalex.org/W1512690422, https://openalex.org/W3000673710, https://openalex.org/W4400441613, https://openalex.org/W4402170913, https://openalex.org/W4309650743, https://openalex.org/W4401338728, https://openalex.org/W4401076392, https://openalex.org/W4310760182, https://openalex.org/W4406289617, https://openalex.org/W4409205823, https://openalex.org/W3207696368, https://openalex.org/W2923682152, https://openalex.org/W3200707343, https://openalex.org/W2911964244, https://openalex.org/W2912802963, https://openalex.org/W4408313058, https://openalex.org/W2972445272, https://openalex.org/W4312064763, https://openalex.org/W2895733290, https://openalex.org/W4388615780, https://openalex.org/W2900090807, https://openalex.org/W1934602670, https://openalex.org/W4384915295, https://openalex.org/W4388007339, https://openalex.org/W4388615774, https://openalex.org/W2080133951, https://openalex.org/W1984994707, https://openalex.org/W4411174340, https://openalex.org/W2151697120, https://openalex.org/W3209680996, https://openalex.org/W3136198265, https://openalex.org/W4399070105, https://openalex.org/W4412187441, https://openalex.org/W4200454878, https://openalex.org/W2119821739, https://openalex.org/W2753863096, https://openalex.org/W1977177161, https://openalex.org/W2970066309, https://openalex.org/W2997591727, https://openalex.org/W4400616986, https://openalex.org/W3144526075, https://openalex.org/W4226189761, https://openalex.org/W4297733535, https://openalex.org/W4286795917 |
| referenced_works_count | 64 |
| abstract_inverted_index.a | 23, 36, 121, 129 |
| abstract_inverted_index.By | 65 |
| abstract_inverted_index.an | 4, 94, 104 |
| abstract_inverted_index.as | 57, 68, 114 |
| abstract_inverted_index.be | 141 |
| abstract_inverted_index.in | 26, 54, 128, 147 |
| abstract_inverted_index.of | 18, 29, 46, 88, 101, 106, 135 |
| abstract_inverted_index.on | 50 |
| abstract_inverted_index.to | 124 |
| abstract_inverted_index.we | 33, 77 |
| abstract_inverted_index.ROC | 98 |
| abstract_inverted_index.and | 62, 103, 149 |
| abstract_inverted_index.for | 7 |
| abstract_inverted_index.the | 16, 27, 43, 63, 97, 144 |
| abstract_inverted_index.area | 95 |
| abstract_inverted_index.deep | 38 |
| abstract_inverted_index.from | 59, 152 |
| abstract_inverted_index.mind | 148 |
| abstract_inverted_index.risk | 17 |
| abstract_inverted_index.show | 78 |
| abstract_inverted_index.size | 146 |
| abstract_inverted_index.such | 113 |
| abstract_inverted_index.that | 41, 79 |
| abstract_inverted_index.will | 150 |
| abstract_inverted_index.with | 11, 93, 143 |
| abstract_inverted_index.(AUC) | 100 |
| abstract_inverted_index.Here, | 32 |
| abstract_inverted_index.based | 49 |
| abstract_inverted_index.broad | 12 |
| abstract_inverted_index.curve | 99 |
| abstract_inverted_index.graph | 73, 80 |
| abstract_inverted_index.major | 24 |
| abstract_inverted_index.risk. | 138 |
| abstract_inverted_index.their | 51 |
| abstract_inverted_index.under | 96 |
| abstract_inverted_index.using | 71 |
| abstract_inverted_index.(GATs) | 84 |
| abstract_inverted_index.0.9600 | 102 |
| abstract_inverted_index.PROTAC | 47, 136 |
| abstract_inverted_index.graphs | 70 |
| abstract_inverted_index.larger | 153 |
| abstract_inverted_index.levels | 53 |
| abstract_inverted_index.neural | 82 |
| abstract_inverted_index.random | 115 |
| abstract_inverted_index.should | 140 |
| abstract_inverted_index.0.6983, | 107 |
| abstract_inverted_index.Results | 139 |
| abstract_inverted_index.achieve | 85 |
| abstract_inverted_index.benefit | 151 |
| abstract_inverted_index.binding | 89 |
| abstract_inverted_index.concern | 25 |
| abstract_inverted_index.dataset | 145 |
| abstract_inverted_index.effects | 92 |
| abstract_inverted_index.forests | 116 |
| abstract_inverted_index.machine | 110 |
| abstract_inverted_index.present | 34 |
| abstract_inverted_index.protein | 9, 20 |
| abstract_inverted_index.remains | 22 |
| abstract_inverted_index.F1-score | 105 |
| abstract_inverted_index.However, | 15 |
| abstract_inverted_index.SENTINEL | 119 |
| abstract_inverted_index.accurate | 86 |
| abstract_inverted_index.chimeras | 1 |
| abstract_inverted_index.emerging | 5 |
| abstract_inverted_index.encoding | 66 |
| abstract_inverted_index.external | 154 |
| abstract_inverted_index.learning | 39, 111 |
| abstract_inverted_index.low-data | 130 |
| abstract_inverted_index.modality | 6 |
| abstract_inverted_index.networks | 75, 83 |
| abstract_inverted_index.predicts | 42 |
| abstract_inverted_index.provides | 120 |
| abstract_inverted_index.scalable | 122 |
| abstract_inverted_index.setting, | 131 |
| abstract_inverted_index.strategy | 123 |
| abstract_inverted_index.targeted | 8 |
| abstract_inverted_index.warheads | 48, 67, 127 |
| abstract_inverted_index.(PAGTNs), | 76 |
| abstract_inverted_index.(PROTACs) | 2 |
| abstract_inverted_index.SENTINEL, | 35 |
| abstract_inverted_index.classical | 109 |
| abstract_inverted_index.databases | 61 |
| abstract_inverted_index.framework | 40 |
| abstract_inverted_index.modelling | 87 |
| abstract_inverted_index.molecular | 69 |
| abstract_inverted_index.represent | 3 |
| abstract_inverted_index.algorithms | 112 |
| abstract_inverted_index.determined | 58 |
| abstract_inverted_index.evaluation | 134 |
| abstract_inverted_index.lower-risk | 126 |
| abstract_inverted_index.off-target | 19, 44, 91, 137 |
| abstract_inverted_index.potential. | 14 |
| abstract_inverted_index.prioritise | 125 |
| abstract_inverted_index.propensity | 45 |
| abstract_inverted_index.supporting | 132 |
| abstract_inverted_index.(AUC=0.840, | 117 |
| abstract_inverted_index.count-based | 90 |
| abstract_inverted_index.degradation | 10, 21 |
| abstract_inverted_index.development | 28 |
| abstract_inverted_index.drug-target | 55 |
| abstract_inverted_index.early-stage | 133 |
| abstract_inverted_index.established | 60 |
| abstract_inverted_index.graph-based | 37 |
| abstract_inverted_index.interpreted | 142 |
| abstract_inverted_index.involvement | 52 |
| abstract_inverted_index.literature. | 64 |
| abstract_inverted_index.therapeutic | 13 |
| abstract_inverted_index.transformer | 74 |
| abstract_inverted_index.validation. | 155 |
| abstract_inverted_index.PROTAC-based | 30 |
| abstract_inverted_index.interactions | 56 |
| abstract_inverted_index.outperforming | 108 |
| abstract_inverted_index.therapeutics. | 31 |
| abstract_inverted_index.path-augmented | 72 |
| abstract_inverted_index.attention-based | 81 |
| abstract_inverted_index.F1-score=0.2778). | 118 |
| abstract_inverted_index.Proteolysis-targeting | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.47030042 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |