Machine Learning for Acute Oral System Toxicity Regression and Classification Article Swipe
conor parks
,
Zied Gaieb
,
Rommie E. Amaro
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv.9733973
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv.9733973
In vivotoxicity testing remains a costly and time-consuming component of any pre-clinical drug development campaign. In particular, LD50 measurements require the loss of animal life but remain a critical component in preventing lethal compounds from entering the clinic. With advances in machine learning, in silicoLD50 prediction now has the potential to greatly reduce this burden. We study various types of machine learning models to predict acute oral LD50 measurements in rats as regression and classification problems. We demonstrate that transfer learning a ResNet34 model pretrained on ImageNet with test time augmentation generates the best performing regression model and that random forest augmented with conformal prediction provides a robust methodology to perform classification.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv.9733973
- OA Status
- gold
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4248988312
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4248988312Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.26434/chemrxiv.9733973Digital Object Identifier
- Title
-
Machine Learning for Acute Oral System Toxicity Regression and ClassificationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-08-28Full publication date if available
- Authors
-
conor parks, Zied Gaieb, Rommie E. AmaroList of authors in order
- Landing page
-
https://doi.org/10.26434/chemrxiv.9733973Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.26434/chemrxiv.9733973Direct OA link when available
- Concepts
-
Machine learning, Random forest, In silico, Artificial intelligence, Regression, Computer science, Component (thermodynamics), Acute toxicity, Support vector machine, Toxicity, Medicine, Statistics, Biology, Mathematics, Internal medicine, Gene, Thermodynamics, Biochemistry, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4248988312 |
|---|---|
| doi | https://doi.org/10.26434/chemrxiv.9733973 |
| ids.doi | https://doi.org/10.26434/chemrxiv.9733973 |
| ids.openalex | https://openalex.org/W4248988312 |
| fwci | 0.0 |
| type | preprint |
| title | Machine Learning for Acute Oral System Toxicity Regression and Classification |
| biblio.issue | |
| biblio.volume | |
| 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 | 0.9876000285148621 |
| 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/T12535 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9013000130653381 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Machine Learning and Data Classification |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C119857082 |
| concepts[0].level | 1 |
| concepts[0].score | 0.7009633779525757 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[0].display_name | Machine learning |
| concepts[1].id | https://openalex.org/C169258074 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6682384610176086 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[1].display_name | Random forest |
| concepts[2].id | https://openalex.org/C2775905019 |
| concepts[2].level | 3 |
| concepts[2].score | 0.6574606895446777 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q192572 |
| concepts[2].display_name | In silico |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5874457955360413 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C83546350 |
| concepts[4].level | 2 |
| concepts[4].score | 0.53007972240448 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1139051 |
| concepts[4].display_name | Regression |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.4843777120113373 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C168167062 |
| concepts[6].level | 2 |
| concepts[6].score | 0.48237863183021545 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1117970 |
| concepts[6].display_name | Component (thermodynamics) |
| concepts[7].id | https://openalex.org/C116263406 |
| concepts[7].level | 3 |
| concepts[7].score | 0.47599828243255615 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q3299024 |
| concepts[7].display_name | Acute toxicity |
| concepts[8].id | https://openalex.org/C12267149 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4514731764793396 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[8].display_name | Support vector machine |
| concepts[9].id | https://openalex.org/C29730261 |
| concepts[9].level | 2 |
| concepts[9].score | 0.3393566608428955 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q274160 |
| concepts[9].display_name | Toxicity |
| concepts[10].id | https://openalex.org/C71924100 |
| concepts[10].level | 0 |
| concepts[10].score | 0.23580092191696167 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[10].display_name | Medicine |
| concepts[11].id | https://openalex.org/C105795698 |
| concepts[11].level | 1 |
| concepts[11].score | 0.18500998616218567 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[11].display_name | Statistics |
| concepts[12].id | https://openalex.org/C86803240 |
| concepts[12].level | 0 |
| concepts[12].score | 0.18096080422401428 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[12].display_name | Biology |
| concepts[13].id | https://openalex.org/C33923547 |
| concepts[13].level | 0 |
| concepts[13].score | 0.17046776413917542 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[13].display_name | Mathematics |
| concepts[14].id | https://openalex.org/C126322002 |
| concepts[14].level | 1 |
| concepts[14].score | 0.07758718729019165 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[14].display_name | Internal medicine |
| concepts[15].id | https://openalex.org/C104317684 |
| concepts[15].level | 2 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[15].display_name | Gene |
| concepts[16].id | https://openalex.org/C97355855 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q11473 |
| concepts[16].display_name | Thermodynamics |
| concepts[17].id | https://openalex.org/C55493867 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[17].display_name | Biochemistry |
| concepts[18].id | https://openalex.org/C121332964 |
| concepts[18].level | 0 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[18].display_name | Physics |
| keywords[0].id | https://openalex.org/keywords/machine-learning |
| keywords[0].score | 0.7009633779525757 |
| keywords[0].display_name | Machine learning |
| keywords[1].id | https://openalex.org/keywords/random-forest |
| keywords[1].score | 0.6682384610176086 |
| keywords[1].display_name | Random forest |
| keywords[2].id | https://openalex.org/keywords/in-silico |
| keywords[2].score | 0.6574606895446777 |
| keywords[2].display_name | In silico |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5874457955360413 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/regression |
| keywords[4].score | 0.53007972240448 |
| keywords[4].display_name | Regression |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.4843777120113373 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/component |
| keywords[6].score | 0.48237863183021545 |
| keywords[6].display_name | Component (thermodynamics) |
| keywords[7].id | https://openalex.org/keywords/acute-toxicity |
| keywords[7].score | 0.47599828243255615 |
| keywords[7].display_name | Acute toxicity |
| keywords[8].id | https://openalex.org/keywords/support-vector-machine |
| keywords[8].score | 0.4514731764793396 |
| keywords[8].display_name | Support vector machine |
| keywords[9].id | https://openalex.org/keywords/toxicity |
| keywords[9].score | 0.3393566608428955 |
| keywords[9].display_name | Toxicity |
| keywords[10].id | https://openalex.org/keywords/medicine |
| keywords[10].score | 0.23580092191696167 |
| keywords[10].display_name | Medicine |
| keywords[11].id | https://openalex.org/keywords/statistics |
| keywords[11].score | 0.18500998616218567 |
| keywords[11].display_name | Statistics |
| keywords[12].id | https://openalex.org/keywords/biology |
| keywords[12].score | 0.18096080422401428 |
| keywords[12].display_name | Biology |
| keywords[13].id | https://openalex.org/keywords/mathematics |
| keywords[13].score | 0.17046776413917542 |
| keywords[13].display_name | Mathematics |
| keywords[14].id | https://openalex.org/keywords/internal-medicine |
| keywords[14].score | 0.07758718729019165 |
| keywords[14].display_name | Internal medicine |
| language | en |
| locations[0].id | doi:10.26434/chemrxiv.9733973 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.26434/chemrxiv.9733973 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5014201274 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8158-5116 |
| authorships[0].author.display_name | conor parks |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I36258959 |
| authorships[0].affiliations[0].raw_affiliation_string | University of California San Diego |
| authorships[0].institutions[0].id | https://openalex.org/I36258959 |
| authorships[0].institutions[0].ror | https://ror.org/0168r3w48 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I36258959 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | University of California, San Diego |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | conor parks |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | University of California San Diego |
| authorships[1].author.id | https://openalex.org/A5064176951 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-7868-5412 |
| authorships[1].author.display_name | Zied Gaieb |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I36258959 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093 |
| authorships[1].institutions[0].id | https://openalex.org/I36258959 |
| authorships[1].institutions[0].ror | https://ror.org/0168r3w48 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I36258959 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | University of California, San Diego |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zied Gaieb |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093 |
| authorships[2].author.id | https://openalex.org/A5081468273 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-9275-9553 |
| authorships[2].author.display_name | Rommie E. Amaro |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I36258959 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093 |
| authorships[2].institutions[0].id | https://openalex.org/I36258959 |
| authorships[2].institutions[0].ror | https://ror.org/0168r3w48 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I36258959 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | University of California, San Diego |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Rommie Amaro |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093 |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.26434/chemrxiv.9733973 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Machine Learning for Acute Oral System Toxicity Regression and Classification |
| 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 | 0.9876000285148621 |
| 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/W4200112873, https://openalex.org/W2955796858, https://openalex.org/W4224941037, https://openalex.org/W2004826645, https://openalex.org/W3135818052, https://openalex.org/W2048488252, https://openalex.org/W4289884158, https://openalex.org/W4288365262, https://openalex.org/W2940614149, https://openalex.org/W2787485953 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.26434/chemrxiv.9733973 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.26434/chemrxiv.9733973 |
| primary_location.id | doi:10.26434/chemrxiv.9733973 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.26434/chemrxiv.9733973 |
| publication_date | 2019-08-28 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W4226150194, https://openalex.org/W2160344370, https://openalex.org/W2794301983, https://openalex.org/W1981276685, https://openalex.org/W4233253307, https://openalex.org/W6675354045, https://openalex.org/W2887381903, https://openalex.org/W2269909407, https://openalex.org/W1994249991, https://openalex.org/W2963446712, https://openalex.org/W2587942483, https://openalex.org/W4297730588, https://openalex.org/W2968395974, https://openalex.org/W2051503575, https://openalex.org/W2290847742, https://openalex.org/W2795247881, https://openalex.org/W2171585602, https://openalex.org/W2041473955, https://openalex.org/W2068542212, https://openalex.org/W2902681872, https://openalex.org/W2150854591, https://openalex.org/W2964113829, https://openalex.org/W2922305141, https://openalex.org/W2618530766, https://openalex.org/W2190612770, https://openalex.org/W1977573154, https://openalex.org/W3104508774, https://openalex.org/W2950238754, https://openalex.org/W2194775991, https://openalex.org/W2888137448, https://openalex.org/W1686810756, https://openalex.org/W2053717624, https://openalex.org/W2101234009, https://openalex.org/W4294502208, https://openalex.org/W2802404464, https://openalex.org/W2610646689, https://openalex.org/W2074621016 |
| referenced_works_count | 37 |
| abstract_inverted_index.a | 4, 27, 81, 106 |
| abstract_inverted_index.In | 15 |
| abstract_inverted_index.We | 55, 76 |
| abstract_inverted_index.as | 71 |
| abstract_inverted_index.in | 30, 40, 69 |
| abstract_inverted_index.of | 9, 22, 59 |
| abstract_inverted_index.on | 85 |
| abstract_inverted_index.to | 50, 63, 109 |
| abstract_inverted_index.and | 6, 73, 97 |
| abstract_inverted_index.any | 10 |
| abstract_inverted_index.but | 25 |
| abstract_inverted_index.has | 47 |
| abstract_inverted_index.now | 46 |
| abstract_inverted_index.the | 20, 36, 48, 92 |
| abstract_inverted_index.LD50 | 17, 67 |
| abstract_inverted_index.With | 38 |
| abstract_inverted_index.best | 93 |
| abstract_inverted_index.drug | 12 |
| abstract_inverted_index.from | 34 |
| abstract_inverted_index.life | 24 |
| abstract_inverted_index.loss | 21 |
| abstract_inverted_index.oral | 66 |
| abstract_inverted_index.rats | 70 |
| abstract_inverted_index.test | 88 |
| abstract_inverted_index.that | 78, 98 |
| abstract_inverted_index.this | 53 |
| abstract_inverted_index.time | 89 |
| abstract_inverted_index.with | 87, 102 |
| abstract_inverted_index.acute | 65 |
| abstract_inverted_index.model | 83, 96 |
| abstract_inverted_index.study | 56 |
| abstract_inverted_index.types | 58 |
| abstract_inverted_index.animal | 23 |
| abstract_inverted_index.costly | 5 |
| abstract_inverted_index.forest | 100 |
| abstract_inverted_index.lethal | 32 |
| abstract_inverted_index.models | 62 |
| abstract_inverted_index.random | 99 |
| abstract_inverted_index.reduce | 52 |
| abstract_inverted_index.remain | 26 |
| abstract_inverted_index.robust | 107 |
| abstract_inverted_index.burden. | 54 |
| abstract_inverted_index.clinic. | 37 |
| abstract_inverted_index.greatly | 51 |
| abstract_inverted_index.machine | 41, 60 |
| abstract_inverted_index.perform | 110 |
| abstract_inverted_index.predict | 64 |
| abstract_inverted_index.remains | 3 |
| abstract_inverted_index.require | 19 |
| abstract_inverted_index.testing | 2 |
| abstract_inverted_index.various | 57 |
| abstract_inverted_index.ImageNet | 86 |
| abstract_inverted_index.ResNet34 | 82 |
| abstract_inverted_index.advances | 39 |
| abstract_inverted_index.critical | 28 |
| abstract_inverted_index.entering | 35 |
| abstract_inverted_index.learning | 61, 80 |
| abstract_inverted_index.provides | 105 |
| abstract_inverted_index.transfer | 79 |
| abstract_inverted_index.augmented | 101 |
| abstract_inverted_index.campaign. | 14 |
| abstract_inverted_index.component | 8, 29 |
| abstract_inverted_index.compounds | 33 |
| abstract_inverted_index.conformal | 103 |
| abstract_inverted_index.generates | 91 |
| abstract_inverted_index.learning, | 42 |
| abstract_inverted_index.potential | 49 |
| abstract_inverted_index.problems. | 75 |
| abstract_inverted_index.performing | 94 |
| abstract_inverted_index.prediction | 45, 104 |
| abstract_inverted_index.pretrained | 84 |
| abstract_inverted_index.preventing | 31 |
| abstract_inverted_index.regression | 72, 95 |
| abstract_inverted_index.<i>in | 43 |
| abstract_inverted_index.demonstrate | 77 |
| abstract_inverted_index.development | 13 |
| abstract_inverted_index.methodology | 108 |
| abstract_inverted_index.particular, | 16 |
| abstract_inverted_index.augmentation | 90 |
| abstract_inverted_index.measurements | 18, 68 |
| abstract_inverted_index.pre-clinical | 11 |
| abstract_inverted_index.classification | 74 |
| abstract_inverted_index.time-consuming | 7 |
| abstract_inverted_index.<p><i>In | 0 |
| abstract_inverted_index.silico</i>LD50 | 44 |
| abstract_inverted_index.vivo</i>toxicity | 1 |
| abstract_inverted_index.classification.</p> | 111 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.30249909 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |