A Structure-Based Platform for Predicting Chemical Reactivity Article Swipe
Frederik Sandfort
,
Felix Strieth‐Kalthoff
,
Marius Kühnemund
,
Christian Beecks
,
Frank Glorius
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv.9981488.v1
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv.9981488.v1
Despite their enormous potential, machine learning methods have only found limited application in predicting reaction outcomes, as current models are often highly complex and, most importantly, are not transferrable to different problem sets. Herein, we present the direct utilization of Lewis structures in a machine learning platform for diverse applications in organic chemistry. Therefore, an input based on multiple fingerprint features (MFF) as a universal molecular representation was developed and used for problem sets of increasing complexity: First, molecular properties across a diverse array of molecules could be predicted accurately. Next, reaction outcomes such as stereoselectivities and yields were predicted for experimental data sets that were previously evaluated using (complex) problem-oriented descriptor models. As a final application, a systematic high-throughput data set showed good correlation when using the MFF model, which suggests that this approach is general and ready for immediate adoption by chemists.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv.9981488.v1
- OA Status
- gold
- Cited By
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4255556435
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4255556435Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.26434/chemrxiv.9981488.v1Digital Object Identifier
- Title
-
A Structure-Based Platform for Predicting Chemical ReactivityWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-10-21Full publication date if available
- Authors
-
Frederik Sandfort, Felix Strieth‐Kalthoff, Marius Kühnemund, Christian Beecks, Frank GloriusList of authors in order
- Landing page
-
https://doi.org/10.26434/chemrxiv.9981488.v1Publisher 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.9981488.v1Direct OA link when available
- Concepts
-
Computer science, Set (abstract data type), Representation (politics), Fingerprint (computing), Machine learning, Data set, Artificial intelligence, Reactivity (psychology), Data mining, Medicine, Programming language, Alternative medicine, Pathology, Law, Political science, PoliticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
50Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 4, 2023: 13, 2022: 9, 2021: 10, 2020: 13Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4255556435 |
|---|---|
| doi | https://doi.org/10.26434/chemrxiv.9981488.v1 |
| ids.doi | https://doi.org/10.26434/chemrxiv.9981488.v1 |
| ids.openalex | https://openalex.org/W4255556435 |
| fwci | 7.06841906 |
| type | preprint |
| title | A Structure-Based Platform for Predicting Chemical Reactivity |
| 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 | 0.9995999932289124 |
| 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/T11948 |
| topics[1].field.id | https://openalex.org/fields/25 |
| topics[1].field.display_name | Materials Science |
| topics[1].score | 0.9991000294685364 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2505 |
| topics[1].subfield.display_name | Materials Chemistry |
| topics[1].display_name | Machine Learning in Materials Science |
| topics[2].id | https://openalex.org/T13180 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.992900013923645 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2304 |
| topics[2].subfield.display_name | Environmental Chemistry |
| topics[2].display_name | Chemistry and Chemical Engineering |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6748074293136597 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C177264268 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6340048313140869 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[1].display_name | Set (abstract data type) |
| concepts[2].id | https://openalex.org/C2776359362 |
| concepts[2].level | 3 |
| concepts[2].score | 0.627412736415863 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2145286 |
| concepts[2].display_name | Representation (politics) |
| concepts[3].id | https://openalex.org/C2777826928 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5046924352645874 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q3745713 |
| concepts[3].display_name | Fingerprint (computing) |
| concepts[4].id | https://openalex.org/C119857082 |
| concepts[4].level | 1 |
| concepts[4].score | 0.4987308979034424 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[4].display_name | Machine learning |
| concepts[5].id | https://openalex.org/C58489278 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4353950619697571 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1172284 |
| concepts[5].display_name | Data set |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.43501532077789307 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C2776910235 |
| concepts[7].level | 3 |
| concepts[7].score | 0.419961541891098 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q18574 |
| concepts[7].display_name | Reactivity (psychology) |
| concepts[8].id | https://openalex.org/C124101348 |
| concepts[8].level | 1 |
| concepts[8].score | 0.33786118030548096 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[8].display_name | Data mining |
| concepts[9].id | https://openalex.org/C71924100 |
| concepts[9].level | 0 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[9].display_name | Medicine |
| concepts[10].id | https://openalex.org/C199360897 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[10].display_name | Programming language |
| concepts[11].id | https://openalex.org/C204787440 |
| concepts[11].level | 2 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q188504 |
| concepts[11].display_name | Alternative medicine |
| concepts[12].id | https://openalex.org/C142724271 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7208 |
| concepts[12].display_name | Pathology |
| concepts[13].id | https://openalex.org/C199539241 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7748 |
| concepts[13].display_name | Law |
| concepts[14].id | https://openalex.org/C17744445 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q36442 |
| concepts[14].display_name | Political science |
| concepts[15].id | https://openalex.org/C94625758 |
| concepts[15].level | 2 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7163 |
| concepts[15].display_name | Politics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.6748074293136597 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/set |
| keywords[1].score | 0.6340048313140869 |
| keywords[1].display_name | Set (abstract data type) |
| keywords[2].id | https://openalex.org/keywords/representation |
| keywords[2].score | 0.627412736415863 |
| keywords[2].display_name | Representation (politics) |
| keywords[3].id | https://openalex.org/keywords/fingerprint |
| keywords[3].score | 0.5046924352645874 |
| keywords[3].display_name | Fingerprint (computing) |
| keywords[4].id | https://openalex.org/keywords/machine-learning |
| keywords[4].score | 0.4987308979034424 |
| keywords[4].display_name | Machine learning |
| keywords[5].id | https://openalex.org/keywords/data-set |
| keywords[5].score | 0.4353950619697571 |
| keywords[5].display_name | Data set |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.43501532077789307 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/reactivity |
| keywords[7].score | 0.419961541891098 |
| keywords[7].display_name | Reactivity (psychology) |
| keywords[8].id | https://openalex.org/keywords/data-mining |
| keywords[8].score | 0.33786118030548096 |
| keywords[8].display_name | Data mining |
| language | en |
| locations[0].id | doi:10.26434/chemrxiv.9981488.v1 |
| 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.9981488.v1 |
| locations[1].id | pmh:oai:figshare.com:article/9981488 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400572 |
| 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 | OPAL (Open@LaTrobe) (La Trobe University) |
| locations[1].source.host_organization | https://openalex.org/I196829312 |
| locations[1].source.host_organization_name | La Trobe University |
| locations[1].source.host_organization_lineage | https://openalex.org/I196829312 |
| locations[1].license | cc-by-nc-nd |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | Text |
| locations[1].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5056295166 |
| authorships[0].author.orcid | https://orcid.org/0009-0009-6941-7860 |
| authorships[0].author.display_name | Frederik Sandfort |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Frederik Sandfort |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5047903834 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-1357-5500 |
| authorships[1].author.display_name | Felix Strieth‐Kalthoff |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Felix Strieth-Kalthoff |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5010863739 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Marius Kühnemund |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Marius Kühnemund |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5039809923 |
| authorships[3].author.orcid | https://orcid.org/0009-0000-9028-629X |
| authorships[3].author.display_name | Christian Beecks |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Christian Beecks |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5017167322 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-0648-956X |
| authorships[4].author.display_name | Frank Glorius |
| authorships[4].countries | DE |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I22465464 |
| authorships[4].affiliations[0].raw_affiliation_string | Westfaelische Wilhelms-Universitaet Muenster |
| authorships[4].institutions[0].id | https://openalex.org/I22465464 |
| authorships[4].institutions[0].ror | https://ror.org/00pd74e08 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I22465464 |
| authorships[4].institutions[0].country_code | DE |
| authorships[4].institutions[0].display_name | University of Münster |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Frank Glorius |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Westfaelische Wilhelms-Universitaet Muenster |
| 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.9981488.v1 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Structure-Based Platform for Predicting Chemical Reactivity |
| 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 | 0.9995999932289124 |
| 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/W3037142710, https://openalex.org/W2418344743, https://openalex.org/W2810948209, https://openalex.org/W1988034520, https://openalex.org/W2091410524, https://openalex.org/W2886028035, https://openalex.org/W1965041116, https://openalex.org/W4282577288, https://openalex.org/W2497660537, https://openalex.org/W3090006914 |
| cited_by_count | 50 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 4 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 13 |
| counts_by_year[2].year | 2022 |
| counts_by_year[2].cited_by_count | 9 |
| counts_by_year[3].year | 2021 |
| counts_by_year[3].cited_by_count | 10 |
| counts_by_year[4].year | 2020 |
| counts_by_year[4].cited_by_count | 13 |
| counts_by_year[5].year | 2019 |
| counts_by_year[5].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.26434/chemrxiv.9981488.v1 |
| 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.9981488.v1 |
| primary_location.id | doi:10.26434/chemrxiv.9981488.v1 |
| 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.9981488.v1 |
| publication_date | 2019-10-21 |
| publication_year | 2019 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 43, 63, 81, 114, 117 |
| abstract_inverted_index.As | 113 |
| abstract_inverted_index.an | 54 |
| abstract_inverted_index.as | 16, 62, 94 |
| abstract_inverted_index.be | 87 |
| abstract_inverted_index.by | 142 |
| abstract_inverted_index.in | 12, 42, 50 |
| abstract_inverted_index.is | 135 |
| abstract_inverted_index.of | 39, 74, 84 |
| abstract_inverted_index.on | 57 |
| abstract_inverted_index.to | 29 |
| abstract_inverted_index.we | 34 |
| abstract_inverted_index.MFF | 128 |
| abstract_inverted_index.and | 69, 96, 137 |
| abstract_inverted_index.are | 19, 26 |
| abstract_inverted_index.for | 47, 71, 100, 139 |
| abstract_inverted_index.not | 27 |
| abstract_inverted_index.set | 121 |
| abstract_inverted_index.the | 36, 127 |
| abstract_inverted_index.was | 67 |
| abstract_inverted_index.and, | 23 |
| abstract_inverted_index.data | 102, 120 |
| abstract_inverted_index.good | 123 |
| abstract_inverted_index.have | 7 |
| abstract_inverted_index.most | 24 |
| abstract_inverted_index.only | 8 |
| abstract_inverted_index.sets | 73, 103 |
| abstract_inverted_index.such | 93 |
| abstract_inverted_index.that | 104, 132 |
| abstract_inverted_index.this | 133 |
| abstract_inverted_index.used | 70 |
| abstract_inverted_index.were | 98, 105 |
| abstract_inverted_index.when | 125 |
| abstract_inverted_index.(MFF) | 61 |
| abstract_inverted_index.Lewis | 40 |
| abstract_inverted_index.Next, | 90 |
| abstract_inverted_index.array | 83 |
| abstract_inverted_index.based | 56 |
| abstract_inverted_index.could | 86 |
| abstract_inverted_index.final | 115 |
| abstract_inverted_index.found | 9 |
| abstract_inverted_index.input | 55 |
| abstract_inverted_index.often | 20 |
| abstract_inverted_index.ready | 138 |
| abstract_inverted_index.sets. | 32 |
| abstract_inverted_index.their | 1 |
| abstract_inverted_index.using | 108, 126 |
| abstract_inverted_index.which | 130 |
| abstract_inverted_index.First, | 77 |
| abstract_inverted_index.across | 80 |
| abstract_inverted_index.direct | 37 |
| abstract_inverted_index.highly | 21 |
| abstract_inverted_index.model, | 129 |
| abstract_inverted_index.models | 18 |
| abstract_inverted_index.showed | 122 |
| abstract_inverted_index.yields | 97 |
| abstract_inverted_index.Despite | 0 |
| abstract_inverted_index.Herein, | 33 |
| abstract_inverted_index.complex | 22 |
| abstract_inverted_index.current | 17 |
| abstract_inverted_index.diverse | 48, 82 |
| abstract_inverted_index.general | 136 |
| abstract_inverted_index.limited | 10 |
| abstract_inverted_index.machine | 4, 44 |
| abstract_inverted_index.methods | 6 |
| abstract_inverted_index.models. | 112 |
| abstract_inverted_index.organic | 51 |
| abstract_inverted_index.present | 35 |
| abstract_inverted_index.problem | 31, 72 |
| abstract_inverted_index.adoption | 141 |
| abstract_inverted_index.approach | 134 |
| abstract_inverted_index.enormous | 2 |
| abstract_inverted_index.features | 60 |
| abstract_inverted_index.learning | 5, 45 |
| abstract_inverted_index.multiple | 58 |
| abstract_inverted_index.outcomes | 92 |
| abstract_inverted_index.platform | 46 |
| abstract_inverted_index.reaction | 14, 91 |
| abstract_inverted_index.suggests | 131 |
| abstract_inverted_index.(complex) | 109 |
| abstract_inverted_index.chemists. | 143 |
| abstract_inverted_index.developed | 68 |
| abstract_inverted_index.different | 30 |
| abstract_inverted_index.evaluated | 107 |
| abstract_inverted_index.immediate | 140 |
| abstract_inverted_index.molecular | 65, 78 |
| abstract_inverted_index.molecules | 85 |
| abstract_inverted_index.outcomes, | 15 |
| abstract_inverted_index.predicted | 88, 99 |
| abstract_inverted_index.universal | 64 |
| abstract_inverted_index.Therefore, | 53 |
| abstract_inverted_index.chemistry. | 52 |
| abstract_inverted_index.descriptor | 111 |
| abstract_inverted_index.increasing | 75 |
| abstract_inverted_index.potential, | 3 |
| abstract_inverted_index.predicting | 13 |
| abstract_inverted_index.previously | 106 |
| abstract_inverted_index.properties | 79 |
| abstract_inverted_index.structures | 41 |
| abstract_inverted_index.systematic | 118 |
| abstract_inverted_index.accurately. | 89 |
| abstract_inverted_index.application | 11 |
| abstract_inverted_index.complexity: | 76 |
| abstract_inverted_index.correlation | 124 |
| abstract_inverted_index.fingerprint | 59 |
| abstract_inverted_index.utilization | 38 |
| abstract_inverted_index.application, | 116 |
| abstract_inverted_index.applications | 49 |
| abstract_inverted_index.experimental | 101 |
| abstract_inverted_index.importantly, | 25 |
| abstract_inverted_index.transferrable | 28 |
| abstract_inverted_index.representation | 66 |
| abstract_inverted_index.high-throughput | 119 |
| abstract_inverted_index.problem-oriented | 110 |
| abstract_inverted_index.stereoselectivities | 95 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.97308946 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |