Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data Article Swipe
Hiroaki Iwata
,
Tatsuru Matsuo
,
Hideaki Mamada
,
Takahisa Motomura
,
Mayumi Matsushita
,
Takeshi Fujiwara
,
Kazuya Maeda
,
Koichi Handa
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jcim.2c00318
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jcim.2c00318
Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CLtot) and steady-state Vd (Vdss) (CLtot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acs.jcim.2c00318
- OA Status
- green
- Cited By
- 48
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4292616203
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4292616203Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1021/acs.jcim.2c00318Digital Object Identifier
- Title
-
Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-22Full publication date if available
- Authors
-
Hiroaki Iwata, Tatsuru Matsuo, Hideaki Mamada, Takahisa Motomura, Mayumi Matsushita, Takeshi Fujiwara, Kazuya Maeda, Koichi HandaList of authors in order
- Landing page
-
https://doi.org/10.1021/acs.jcim.2c00318Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/9472274Direct OA link when available
- Concepts
-
Imputation (statistics), Pharmacokinetics, Animal model, Volume of distribution, Missing data, Profiling (computer programming), Computer science, Statistics, Machine learning, Mathematics, Pharmacology, Medicine, Internal medicine, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
48Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 20, 2024: 17, 2023: 11Per-year citation counts (last 5 years)
- References (count)
-
44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4292616203 |
|---|---|
| doi | https://doi.org/10.1021/acs.jcim.2c00318 |
| ids.doi | https://doi.org/10.1021/acs.jcim.2c00318 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/35993595 |
| ids.openalex | https://openalex.org/W4292616203 |
| fwci | 12.64069265 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000818 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Animals |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D000084342 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Drug Elimination Routes |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D006801 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Humans |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D000069550 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Machine Learning |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D008954 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Models, Biological |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D004364 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Pharmaceutical Preparations |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D000818 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Animals |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D000084342 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Drug Elimination Routes |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D006801 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Humans |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D000069550 |
| mesh[9].is_major_topic | True |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Machine Learning |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D008954 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Models, Biological |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D004364 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Pharmaceutical Preparations |
| type | article |
| title | Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data |
| biblio.issue | 17 |
| biblio.volume | 62 |
| biblio.last_page | 4065 |
| biblio.first_page | 4057 |
| 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 | 0.9998999834060669 |
| 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/T10375 |
| topics[1].field.id | https://openalex.org/fields/30 |
| topics[1].field.display_name | Pharmacology, Toxicology and Pharmaceutics |
| topics[1].score | 0.9937000274658203 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3004 |
| topics[1].subfield.display_name | Pharmacology |
| topics[1].display_name | Pharmacogenetics and Drug Metabolism |
| topics[2].id | https://openalex.org/T10836 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9811000227928162 |
| 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 | Metabolomics and Mass Spectrometry Studies |
| funders[0].id | https://openalex.org/F4320321034 |
| funders[0].ror | https://ror.org/0055k7a87 |
| funders[0].display_name | New Energy and Industrial Technology Development Organization |
| funders[1].id | https://openalex.org/F4320322985 |
| funders[1].ror | |
| funders[1].display_name | Fugaku Trust for Medicinal Research |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C58041806 |
| concepts[0].level | 3 |
| concepts[0].score | 0.6420507431030273 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1660484 |
| concepts[0].display_name | Imputation (statistics) |
| concepts[1].id | https://openalex.org/C112705442 |
| concepts[1].level | 2 |
| concepts[1].score | 0.501173734664917 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q323936 |
| concepts[1].display_name | Pharmacokinetics |
| concepts[2].id | https://openalex.org/C2778117688 |
| concepts[2].level | 2 |
| concepts[2].score | 0.4881180226802826 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q264024 |
| concepts[2].display_name | Animal model |
| concepts[3].id | https://openalex.org/C139254425 |
| concepts[3].level | 3 |
| concepts[3].score | 0.46783626079559326 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1332167 |
| concepts[3].display_name | Volume of distribution |
| concepts[4].id | https://openalex.org/C9357733 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4658753573894501 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q6878417 |
| concepts[4].display_name | Missing data |
| concepts[5].id | https://openalex.org/C187191949 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4378739595413208 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1138496 |
| concepts[5].display_name | Profiling (computer programming) |
| concepts[6].id | https://openalex.org/C41008148 |
| concepts[6].level | 0 |
| concepts[6].score | 0.43480485677719116 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[6].display_name | Computer science |
| concepts[7].id | https://openalex.org/C105795698 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3980981409549713 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[7].display_name | Statistics |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3138082027435303 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C33923547 |
| concepts[9].level | 0 |
| concepts[9].score | 0.3021378815174103 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[9].display_name | Mathematics |
| concepts[10].id | https://openalex.org/C98274493 |
| concepts[10].level | 1 |
| concepts[10].score | 0.2645717263221741 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q128406 |
| concepts[10].display_name | Pharmacology |
| concepts[11].id | https://openalex.org/C71924100 |
| concepts[11].level | 0 |
| concepts[11].score | 0.2595568895339966 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[11].display_name | Medicine |
| concepts[12].id | https://openalex.org/C126322002 |
| concepts[12].level | 1 |
| concepts[12].score | 0.10177144408226013 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[12].display_name | Internal medicine |
| concepts[13].id | https://openalex.org/C111919701 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[13].display_name | Operating system |
| keywords[0].id | https://openalex.org/keywords/imputation |
| keywords[0].score | 0.6420507431030273 |
| keywords[0].display_name | Imputation (statistics) |
| keywords[1].id | https://openalex.org/keywords/pharmacokinetics |
| keywords[1].score | 0.501173734664917 |
| keywords[1].display_name | Pharmacokinetics |
| keywords[2].id | https://openalex.org/keywords/animal-model |
| keywords[2].score | 0.4881180226802826 |
| keywords[2].display_name | Animal model |
| keywords[3].id | https://openalex.org/keywords/volume-of-distribution |
| keywords[3].score | 0.46783626079559326 |
| keywords[3].display_name | Volume of distribution |
| keywords[4].id | https://openalex.org/keywords/missing-data |
| keywords[4].score | 0.4658753573894501 |
| keywords[4].display_name | Missing data |
| keywords[5].id | https://openalex.org/keywords/profiling |
| keywords[5].score | 0.4378739595413208 |
| keywords[5].display_name | Profiling (computer programming) |
| keywords[6].id | https://openalex.org/keywords/computer-science |
| keywords[6].score | 0.43480485677719116 |
| keywords[6].display_name | Computer science |
| keywords[7].id | https://openalex.org/keywords/statistics |
| keywords[7].score | 0.3980981409549713 |
| keywords[7].display_name | Statistics |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.3138082027435303 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/mathematics |
| keywords[9].score | 0.3021378815174103 |
| keywords[9].display_name | Mathematics |
| keywords[10].id | https://openalex.org/keywords/pharmacology |
| keywords[10].score | 0.2645717263221741 |
| keywords[10].display_name | Pharmacology |
| keywords[11].id | https://openalex.org/keywords/medicine |
| keywords[11].score | 0.2595568895339966 |
| keywords[11].display_name | Medicine |
| keywords[12].id | https://openalex.org/keywords/internal-medicine |
| keywords[12].score | 0.10177144408226013 |
| keywords[12].display_name | Internal medicine |
| language | en |
| locations[0].id | doi:10.1021/acs.jcim.2c00318 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S167262187 |
| locations[0].source.issn | 1549-9596, 1549-960X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1549-9596 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Journal of Chemical Information and Modeling |
| locations[0].source.host_organization | https://openalex.org/P4310320006 |
| locations[0].source.host_organization_name | American Chemical Society |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320006 |
| locations[0].source.host_organization_lineage_names | American Chemical Society |
| 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 | Journal of Chemical Information and Modeling |
| locations[0].landing_page_url | https://doi.org/10.1021/acs.jcim.2c00318 |
| locations[1].id | pmid:35993595 |
| 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 | Journal of chemical information and modeling |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/35993595 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:9472274 |
| 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 | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | J Chem Inf Model |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/9472274 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5043360512 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9791-0008 |
| authorships[0].author.display_name | Hiroaki Iwata |
| authorships[0].countries | JP |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I22299242 |
| authorships[0].affiliations[0].raw_affiliation_string | Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan |
| authorships[0].institutions[0].id | https://openalex.org/I22299242 |
| authorships[0].institutions[0].ror | https://ror.org/02kpeqv85 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I22299242 |
| authorships[0].institutions[0].country_code | JP |
| authorships[0].institutions[0].display_name | Kyoto University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hiroaki Iwata |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan |
| authorships[1].author.id | https://openalex.org/A5038756853 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Tatsuru Matsuo |
| authorships[1].countries | JP |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I2252096349 |
| authorships[1].affiliations[0].raw_affiliation_string | Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki-shi, Kanagawa 211-8588, Japan |
| authorships[1].institutions[0].id | https://openalex.org/I2252096349 |
| authorships[1].institutions[0].ror | https://ror.org/038e2g226 |
| authorships[1].institutions[0].type | company |
| authorships[1].institutions[0].lineage | https://openalex.org/I2252096349 |
| authorships[1].institutions[0].country_code | JP |
| authorships[1].institutions[0].display_name | Fujitsu (Japan) |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Tatsuru Matsuo |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki-shi, Kanagawa 211-8588, Japan |
| authorships[2].author.id | https://openalex.org/A5013907089 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5433-7042 |
| authorships[2].author.display_name | Hideaki Mamada |
| authorships[2].countries | JP |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210108598 |
| authorships[2].affiliations[0].raw_affiliation_string | DMPK Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho, Takatsuki, Osaka 569-1125, Japan |
| authorships[2].institutions[0].id | https://openalex.org/I4210108598 |
| authorships[2].institutions[0].ror | https://ror.org/01xdq1k91 |
| authorships[2].institutions[0].type | company |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210108598 |
| authorships[2].institutions[0].country_code | JP |
| authorships[2].institutions[0].display_name | Japan Tobacco (Japan) |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Hideaki Mamada |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | DMPK Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho, Takatsuki, Osaka 569-1125, Japan |
| authorships[3].author.id | https://openalex.org/A5054763123 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Takahisa Motomura |
| authorships[3].countries | JP |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210108598 |
| authorships[3].affiliations[0].raw_affiliation_string | Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho, Takatsuki, Osaka 569-1125, Japan |
| authorships[3].institutions[0].id | https://openalex.org/I4210108598 |
| authorships[3].institutions[0].ror | https://ror.org/01xdq1k91 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210108598 |
| authorships[3].institutions[0].country_code | JP |
| authorships[3].institutions[0].display_name | Japan Tobacco (Japan) |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Takahisa Motomura |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho, Takatsuki, Osaka 569-1125, Japan |
| authorships[4].author.id | https://openalex.org/A5052265315 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Mayumi Matsushita |
| authorships[4].countries | JP |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I2252096349 |
| authorships[4].affiliations[0].raw_affiliation_string | Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki-shi, Kanagawa 211-8588, Japan |
| authorships[4].institutions[0].id | https://openalex.org/I2252096349 |
| authorships[4].institutions[0].ror | https://ror.org/038e2g226 |
| authorships[4].institutions[0].type | company |
| authorships[4].institutions[0].lineage | https://openalex.org/I2252096349 |
| authorships[4].institutions[0].country_code | JP |
| authorships[4].institutions[0].display_name | Fujitsu (Japan) |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Mayumi Matsushita |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki-shi, Kanagawa 211-8588, Japan |
| authorships[5].author.id | https://openalex.org/A5101442710 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-5754-8443 |
| authorships[5].author.display_name | Takeshi Fujiwara |
| authorships[5].countries | JP |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I22299242 |
| authorships[5].affiliations[0].raw_affiliation_string | Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan |
| authorships[5].institutions[0].id | https://openalex.org/I22299242 |
| authorships[5].institutions[0].ror | https://ror.org/02kpeqv85 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I22299242 |
| authorships[5].institutions[0].country_code | JP |
| authorships[5].institutions[0].display_name | Kyoto University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Takeshi Fujiwara |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan |
| authorships[6].author.id | https://openalex.org/A5089688722 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-9174-5213 |
| authorships[6].author.display_name | Kazuya Maeda |
| authorships[6].countries | JP |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I74801974 |
| authorships[6].affiliations[0].raw_affiliation_string | Graduate School of Pharmaceutical Sciences, Department of Molecular Pharmacokinetics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan |
| authorships[6].institutions[0].id | https://openalex.org/I74801974 |
| authorships[6].institutions[0].ror | https://ror.org/057zh3y96 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I74801974 |
| authorships[6].institutions[0].country_code | JP |
| authorships[6].institutions[0].display_name | The University of Tokyo |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Kazuya Maeda |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Graduate School of Pharmaceutical Sciences, Department of Molecular Pharmacokinetics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan |
| authorships[7].author.id | https://openalex.org/A5045140841 |
| authorships[7].author.orcid | https://orcid.org/0000-0003-2748-9742 |
| authorships[7].author.display_name | Koichi Handa |
| authorships[7].countries | JP |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I90469020 |
| authorships[7].affiliations[0].raw_affiliation_string | Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan |
| authorships[7].institutions[0].id | https://openalex.org/I90469020 |
| authorships[7].institutions[0].ror | https://ror.org/038kxkq33 |
| authorships[7].institutions[0].type | company |
| authorships[7].institutions[0].lineage | https://openalex.org/I90469020 |
| authorships[7].institutions[0].country_code | JP |
| authorships[7].institutions[0].display_name | Teijin (Japan) |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Koichi Handa |
| authorships[7].is_corresponding | True |
| authorships[7].raw_affiliation_strings | Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.ncbi.nlm.nih.gov/pmc/articles/9472274 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data |
| 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 | 0.9998999834060669 |
| 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/W2181530120, https://openalex.org/W4211215373, https://openalex.org/W2024529227, https://openalex.org/W2055961818, https://openalex.org/W2903115227, https://openalex.org/W1574575415, https://openalex.org/W3144172081, https://openalex.org/W3179858851, https://openalex.org/W3028371478, https://openalex.org/W2081476516 |
| cited_by_count | 48 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 20 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 17 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 11 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:pubmedcentral.nih.gov:9472274 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764455111 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | PubMed Central |
| best_oa_location.source.host_organization | https://openalex.org/I1299303238 |
| best_oa_location.source.host_organization_name | National Institutes of Health |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | J Chem Inf Model |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/9472274 |
| primary_location.id | doi:10.1021/acs.jcim.2c00318 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S167262187 |
| primary_location.source.issn | 1549-9596, 1549-960X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1549-9596 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Journal of Chemical Information and Modeling |
| primary_location.source.host_organization | https://openalex.org/P4310320006 |
| primary_location.source.host_organization_name | American Chemical Society |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320006 |
| primary_location.source.host_organization_lineage_names | American Chemical Society |
| 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 | Journal of Chemical Information and Modeling |
| primary_location.landing_page_url | https://doi.org/10.1021/acs.jcim.2c00318 |
| publication_date | 2022-08-22 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2007465986, https://openalex.org/W2560056856, https://openalex.org/W2511526876, https://openalex.org/W2165417769, https://openalex.org/W2133622314, https://openalex.org/W2158185292, https://openalex.org/W2114672790, https://openalex.org/W2589072694, https://openalex.org/W3196566788, https://openalex.org/W3135873052, https://openalex.org/W2968219050, https://openalex.org/W2012929832, https://openalex.org/W2075775075, https://openalex.org/W3030834496, https://openalex.org/W3211668464, https://openalex.org/W3124422520, https://openalex.org/W2096863518, https://openalex.org/W2031668066, https://openalex.org/W2064186732, https://openalex.org/W2210241103, https://openalex.org/W2972499938, https://openalex.org/W2789223439, https://openalex.org/W2096541451, https://openalex.org/W2177317049, https://openalex.org/W2767891136, https://openalex.org/W2036846365, https://openalex.org/W3016106445, https://openalex.org/W2911964244, https://openalex.org/W2244501064, https://openalex.org/W2138212930, https://openalex.org/W2042766684, https://openalex.org/W2954500169, https://openalex.org/W2888313355, https://openalex.org/W1946336696, https://openalex.org/W2009858196, https://openalex.org/W2114871267, https://openalex.org/W1982652088, https://openalex.org/W1983118095, https://openalex.org/W2113830209, https://openalex.org/W2026015795, https://openalex.org/W3088169840, https://openalex.org/W2559542005, https://openalex.org/W1836465849, https://openalex.org/W1655460658 |
| referenced_works_count | 44 |
| abstract_inverted_index.a | 96 |
| abstract_inverted_index.In | 105 |
| abstract_inverted_index.Vd | 146 |
| abstract_inverted_index.We | 134 |
| abstract_inverted_index.an | 3 |
| abstract_inverted_index.as | 90, 200 |
| abstract_inverted_index.in | 6 |
| abstract_inverted_index.is | 64 |
| abstract_inverted_index.it | 63, 187 |
| abstract_inverted_index.of | 9, 14, 23, 30, 99, 122, 132 |
| abstract_inverted_index.on | 115 |
| abstract_inverted_index.to | 46, 50, 59, 66, 101, 118, 172, 195 |
| abstract_inverted_index.we | 108 |
| abstract_inverted_index.and | 28, 40, 76, 112, 125, 144 |
| abstract_inverted_index.are | 18, 33, 79 |
| abstract_inverted_index.can | 93 |
| abstract_inverted_index.for | 20, 36, 129, 139 |
| abstract_inverted_index.new | 10 |
| abstract_inverted_index.not | 189 |
| abstract_inverted_index.the | 7, 21, 120, 173 |
| abstract_inverted_index.use | 47 |
| abstract_inverted_index.(CL) | 27 |
| abstract_inverted_index.(Vd) | 32 |
| abstract_inverted_index.This | 83 |
| abstract_inverted_index.body | 141 |
| abstract_inverted_index.data | 71, 89, 117 |
| abstract_inverted_index.does | 188 |
| abstract_inverted_index.fold | 151 |
| abstract_inverted_index.from | 54, 72, 182 |
| abstract_inverted_index.have | 44 |
| abstract_inverted_index.many | 41, 77 |
| abstract_inverted_index.mean | 150 |
| abstract_inverted_index.more | 197 |
| abstract_inverted_index.only | 94 |
| abstract_inverted_index.role | 5 |
| abstract_inverted_index.that | 85 |
| abstract_inverted_index.this | 106, 179 |
| abstract_inverted_index.time | 201 |
| abstract_inverted_index.were | 170 |
| abstract_inverted_index.1.64; | 162 |
| abstract_inverted_index.1.92; | 154 |
| abstract_inverted_index.GMFE, | 161 |
| abstract_inverted_index.Then, | 178 |
| abstract_inverted_index.These | 168 |
| abstract_inverted_index.apply | 95 |
| abstract_inverted_index.could | 135 |
| abstract_inverted_index.error | 152 |
| abstract_inverted_index.human | 15, 60 |
| abstract_inverted_index.kinds | 131 |
| abstract_inverted_index.means | 84 |
| abstract_inverted_index.model | 103 |
| abstract_inverted_index.novel | 137 |
| abstract_inverted_index.plays | 2 |
| abstract_inverted_index.small | 97 |
| abstract_inverted_index.their | 102 |
| abstract_inverted_index.these | 52, 73, 130 |
| abstract_inverted_index.total | 140 |
| abstract_inverted_index.using | 87 |
| abstract_inverted_index.which | 193 |
| abstract_inverted_index.2-fold | 157, 165 |
| abstract_inverted_index.66.5%; | 159 |
| abstract_inverted_index.animal | 57, 74, 175, 183, 191 |
| abstract_inverted_index.become | 196 |
| abstract_inverted_index.drugs. | 11 |
| abstract_inverted_index.error, | 158, 166 |
| abstract_inverted_index.method | 180 |
| abstract_inverted_index.models | 58, 138 |
| abstract_inverted_index.number | 98, 121 |
| abstract_inverted_index.obtain | 67, 136 |
| abstract_inverted_index.study, | 107 |
| abstract_inverted_index.values | 53 |
| abstract_inverted_index.volume | 29 |
| abstract_inverted_index.within | 156, 164 |
| abstract_inverted_index.71.1%). | 167 |
| abstract_inverted_index.[GMFE], | 153 |
| abstract_inverted_index.because | 186 |
| abstract_inverted_index.differs | 181 |
| abstract_inverted_index.factors | 35 |
| abstract_inverted_index.feature | 113 |
| abstract_inverted_index.methods | 49, 185 |
| abstract_inverted_index.missing | 80 |
| abstract_inverted_index.models, | 75 |
| abstract_inverted_index.models. | 177 |
| abstract_inverted_index.passes. | 202 |
| abstract_inverted_index.perform | 109 |
| abstract_inverted_index.require | 190 |
| abstract_inverted_index.studies | 43, 78, 86 |
| abstract_inverted_index.success | 22 |
| abstract_inverted_index.trials. | 25 |
| abstract_inverted_index.values. | 82 |
| abstract_inverted_index.Accurate | 12 |
| abstract_inverted_index.However, | 62 |
| abstract_inverted_index.clinical | 24 |
| abstract_inverted_index.continue | 194 |
| abstract_inverted_index.critical | 81 |
| abstract_inverted_index.datasets | 127 |
| abstract_inverted_index.increase | 119 |
| abstract_inverted_index.previous | 42 |
| abstract_inverted_index.research | 1 |
| abstract_inverted_index.scale-up | 176, 184 |
| abstract_inverted_index.strictly | 198 |
| abstract_inverted_index.studies. | 133 |
| abstract_inverted_index.training | 123 |
| abstract_inverted_index.Clearance | 26 |
| abstract_inverted_index.attempted | 45 |
| abstract_inverted_index.available | 128 |
| abstract_inverted_index.clearance | 142 |
| abstract_inverted_index.compounds | 100, 124 |
| abstract_inverted_index.difficult | 65 |
| abstract_inverted_index.essential | 19 |
| abstract_inverted_index.geometric | 149 |
| abstract_inverted_index.important | 4, 34 |
| abstract_inverted_index.regulated | 199 |
| abstract_inverted_index.selection | 114 |
| abstract_inverted_index.subjects. | 61 |
| abstract_inverted_index.training. | 104 |
| abstract_inverted_index.variables | 92 |
| abstract_inverted_index.accuracies | 169 |
| abstract_inverted_index.comparable | 171 |
| abstract_inverted_index.evaluating | 37 |
| abstract_inverted_index.imputation | 111 |
| abstract_inverted_index.laboratory | 56 |
| abstract_inverted_index.parameters | 17 |
| abstract_inverted_index.percentage | 155, 163 |
| abstract_inverted_index.development | 8 |
| abstract_inverted_index.explanatory | 91 |
| abstract_inverted_index.extrapolate | 51 |
| abstract_inverted_index.nonclinical | 55, 88, 116, 126 |
| abstract_inverted_index.predictions | 13 |
| abstract_inverted_index.properties, | 39 |
| abstract_inverted_index.sufficient, | 68 |
| abstract_inverted_index.conventional | 174 |
| abstract_inverted_index.distribution | 31 |
| abstract_inverted_index.experimental | 70 |
| abstract_inverted_index.experiments, | 192 |
| abstract_inverted_index.steady-state | 145 |
| abstract_inverted_index.comprehensive | 69 |
| abstract_inverted_index.computational | 48 |
| abstract_inverted_index.missing-value | 110 |
| abstract_inverted_index.Pharmacokinetic | 0 |
| abstract_inverted_index.pharmacokinetic | 16, 38 |
| abstract_inverted_index.Vd<sub>ss</sub>: | 160 |
| abstract_inverted_index.(Vd<sub>ss</sub>) | 147 |
| abstract_inverted_index.(CL<sub>tot</sub>) | 143 |
| abstract_inverted_index.(CL<sub>tot</sub>: | 148 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5043360512, https://openalex.org/A5045140841 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I22299242, https://openalex.org/I90469020 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.97923451 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |