Machine Learning Application for Medicinal Chemistry: Colchicine Case, New Structures, and Anticancer Activity Prediction Article Swipe
Damian Nowak
,
Adam Huczyński
,
Rafał A. Bachorz
,
Marcin Hoffmann
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ph17020173
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ph17020173
In the contemporary era, the exploration of machine learning (ML) has gained widespread attention and is being leveraged to augment traditional methodologies in quantitative structure–activity relationship (QSAR) investigations. The principal objective of this research was to assess the anticancer potential of colchicine-based compounds across five distinct cell lines. This research endeavor ultimately sought to construct ML models proficient in forecasting anticancer activity as quantified by the IC50 value, while concurrently generating innovative colchicine-derived compounds. The resistance index (RI) is computed to evaluate the drug resistance exhibited by LoVo/DX cells relative to LoVo cancer cell lines. Meanwhile, the selectivity index (SI) is computed to determine the potential of a compound to demonstrate superior efficacy against tumor cells compared to its toxicity against normal cells, such as BALB/3T3. We introduce a novel ML system adept at recommending novel chemical structures predicated on known anticancer activity. Our investigation entailed the assessment of inhibitory capabilities across five cell lines, employing predictive models utilizing various algorithms, including random forest, decision tree, support vector machines, k-nearest neighbors, and multiple linear regression. The most proficient model, as determined by quality metrics, was employed to predict the anticancer activity of novel colchicine-based compounds. This methodological approach yielded the establishment of a library encompassing new colchicine-based compounds, each assigned an IC50 value. Additionally, this study resulted in the development of a validated predictive model, capable of reasonably estimating IC50 values based on molecular structure input.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ph17020173
- https://www.mdpi.com/1424-8247/17/2/173/pdf?version=1706545606
- OA Status
- gold
- Cited By
- 6
- References
- 70
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391309329
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4391309329Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ph17020173Digital Object Identifier
- Title
-
Machine Learning Application for Medicinal Chemistry: Colchicine Case, New Structures, and Anticancer Activity PredictionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-29Full publication date if available
- Authors
-
Damian Nowak, Adam Huczyński, Rafał A. Bachorz, Marcin HoffmannList of authors in order
- Landing page
-
https://doi.org/10.3390/ph17020173Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8247/17/2/173/pdf?version=1706545606Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8247/17/2/173/pdf?version=1706545606Direct OA link when available
- Concepts
-
Random forest, Machine learning, Decision tree, Colchicine, Quantitative structure–activity relationship, IC50, Computer science, Artificial intelligence, Support vector machine, Computational biology, Chemistry, Biology, In vitro, Biochemistry, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6Per-year citation counts (last 5 years)
- References (count)
-
70Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4391309329 |
|---|---|
| doi | https://doi.org/10.3390/ph17020173 |
| ids.doi | https://doi.org/10.3390/ph17020173 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/38399388 |
| ids.openalex | https://openalex.org/W4391309329 |
| fwci | 4.74081345 |
| type | article |
| title | Machine Learning Application for Medicinal Chemistry: Colchicine Case, New Structures, and Anticancer Activity Prediction |
| biblio.issue | 2 |
| biblio.volume | 17 |
| biblio.last_page | 173 |
| biblio.first_page | 173 |
| topics[0].id | https://openalex.org/T10211 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1703 |
| topics[0].subfield.display_name | Computational Theory and Mathematics |
| topics[0].display_name | Computational Drug Discovery Methods |
| topics[1].id | https://openalex.org/T10274 |
| topics[1].field.id | https://openalex.org/fields/16 |
| topics[1].field.display_name | Chemistry |
| topics[1].score | 0.9919000267982483 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1605 |
| topics[1].subfield.display_name | Organic Chemistry |
| topics[1].display_name | Synthesis and biological activity |
| topics[2].id | https://openalex.org/T10252 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9225999712944031 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2736 |
| topics[2].subfield.display_name | Pharmacology |
| topics[2].display_name | Microbial Natural Products and Biosynthesis |
| is_xpac | False |
| apc_list.value | 2000 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2165 |
| apc_paid.value | 2000 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2165 |
| concepts[0].id | https://openalex.org/C169258074 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6055366396903992 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[0].display_name | Random forest |
| concepts[1].id | https://openalex.org/C119857082 |
| concepts[1].level | 1 |
| concepts[1].score | 0.5806177258491516 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[1].display_name | Machine learning |
| concepts[2].id | https://openalex.org/C84525736 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5753272771835327 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q831366 |
| concepts[2].display_name | Decision tree |
| concepts[3].id | https://openalex.org/C2776636253 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5546568036079407 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q326224 |
| concepts[3].display_name | Colchicine |
| concepts[4].id | https://openalex.org/C164126121 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5436039566993713 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q766383 |
| concepts[4].display_name | Quantitative structure–activity relationship |
| concepts[5].id | https://openalex.org/C2777752497 |
| concepts[5].level | 3 |
| concepts[5].score | 0.5125267505645752 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q286779 |
| concepts[5].display_name | IC50 |
| concepts[6].id | https://openalex.org/C41008148 |
| concepts[6].level | 0 |
| concepts[6].score | 0.511961817741394 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[6].display_name | Computer science |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4800173342227936 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C12267149 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4668821692466736 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[8].display_name | Support vector machine |
| concepts[9].id | https://openalex.org/C70721500 |
| concepts[9].level | 1 |
| concepts[9].score | 0.37990981340408325 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q177005 |
| concepts[9].display_name | Computational biology |
| concepts[10].id | https://openalex.org/C185592680 |
| concepts[10].level | 0 |
| concepts[10].score | 0.30629539489746094 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[10].display_name | Chemistry |
| concepts[11].id | https://openalex.org/C86803240 |
| concepts[11].level | 0 |
| concepts[11].score | 0.2252633273601532 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[11].display_name | Biology |
| concepts[12].id | https://openalex.org/C202751555 |
| concepts[12].level | 2 |
| concepts[12].score | 0.16118714213371277 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q221681 |
| concepts[12].display_name | In vitro |
| concepts[13].id | https://openalex.org/C55493867 |
| concepts[13].level | 1 |
| concepts[13].score | 0.12707042694091797 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[13].display_name | Biochemistry |
| concepts[14].id | https://openalex.org/C54355233 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7162 |
| concepts[14].display_name | Genetics |
| keywords[0].id | https://openalex.org/keywords/random-forest |
| keywords[0].score | 0.6055366396903992 |
| keywords[0].display_name | Random forest |
| keywords[1].id | https://openalex.org/keywords/machine-learning |
| keywords[1].score | 0.5806177258491516 |
| keywords[1].display_name | Machine learning |
| keywords[2].id | https://openalex.org/keywords/decision-tree |
| keywords[2].score | 0.5753272771835327 |
| keywords[2].display_name | Decision tree |
| keywords[3].id | https://openalex.org/keywords/colchicine |
| keywords[3].score | 0.5546568036079407 |
| keywords[3].display_name | Colchicine |
| keywords[4].id | https://openalex.org/keywords/quantitative-structure–activity-relationship |
| keywords[4].score | 0.5436039566993713 |
| keywords[4].display_name | Quantitative structure–activity relationship |
| keywords[5].id | https://openalex.org/keywords/ic50 |
| keywords[5].score | 0.5125267505645752 |
| keywords[5].display_name | IC50 |
| keywords[6].id | https://openalex.org/keywords/computer-science |
| keywords[6].score | 0.511961817741394 |
| keywords[6].display_name | Computer science |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.4800173342227936 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/support-vector-machine |
| keywords[8].score | 0.4668821692466736 |
| keywords[8].display_name | Support vector machine |
| keywords[9].id | https://openalex.org/keywords/computational-biology |
| keywords[9].score | 0.37990981340408325 |
| keywords[9].display_name | Computational biology |
| keywords[10].id | https://openalex.org/keywords/chemistry |
| keywords[10].score | 0.30629539489746094 |
| keywords[10].display_name | Chemistry |
| keywords[11].id | https://openalex.org/keywords/biology |
| keywords[11].score | 0.2252633273601532 |
| keywords[11].display_name | Biology |
| keywords[12].id | https://openalex.org/keywords/in-vitro |
| keywords[12].score | 0.16118714213371277 |
| keywords[12].display_name | In vitro |
| keywords[13].id | https://openalex.org/keywords/biochemistry |
| keywords[13].score | 0.12707042694091797 |
| keywords[13].display_name | Biochemistry |
| language | en |
| locations[0].id | doi:10.3390/ph17020173 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S16322639 |
| locations[0].source.issn | 1424-8247 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1424-8247 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Pharmaceuticals |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1424-8247/17/2/173/pdf?version=1706545606 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Pharmaceuticals |
| locations[0].landing_page_url | https://doi.org/10.3390/ph17020173 |
| locations[1].id | pmid:38399388 |
| 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 | Pharmaceuticals (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/38399388 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:10892630 |
| 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 | cc-by |
| locations[2].pdf_url | https://pmc.ncbi.nlm.nih.gov/articles/PMC10892630/pdf/pharmaceuticals-17-00173.pdf |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Pharmaceuticals (Basel) |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/10892630 |
| locations[3].id | pmh:oai:doaj.org/article:164f5a9b7c7047fa8fdff12b52081ca5 |
| locations[3].is_oa | False |
| locations[3].source.id | https://openalex.org/S4306401280 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[3].source.host_organization | |
| locations[3].source.host_organization_name | |
| locations[3].license | |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | article |
| locations[3].license_id | |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Pharmaceuticals, Vol 17, Iss 2, p 173 (2024) |
| locations[3].landing_page_url | https://doaj.org/article/164f5a9b7c7047fa8fdff12b52081ca5 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5046613987 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-3739-3306 |
| authorships[0].author.display_name | Damian Nowak |
| authorships[0].countries | PL |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I59411706 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Quantum Chemistry, Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland |
| authorships[0].institutions[0].id | https://openalex.org/I59411706 |
| authorships[0].institutions[0].ror | https://ror.org/04g6bbq64 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I59411706 |
| authorships[0].institutions[0].country_code | PL |
| authorships[0].institutions[0].display_name | Adam Mickiewicz University in Poznań |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Damian Nowak |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Department of Quantum Chemistry, Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland |
| authorships[1].author.id | https://openalex.org/A5028053146 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4770-215X |
| authorships[1].author.display_name | Adam Huczyński |
| authorships[1].countries | PL |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I59411706 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Medical Chemistry, Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland |
| authorships[1].institutions[0].id | https://openalex.org/I59411706 |
| authorships[1].institutions[0].ror | https://ror.org/04g6bbq64 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I59411706 |
| authorships[1].institutions[0].country_code | PL |
| authorships[1].institutions[0].display_name | Adam Mickiewicz University in Poznań |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Adam Huczyński |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Medical Chemistry, Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland |
| authorships[2].author.id | https://openalex.org/A5022251442 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-6940-9432 |
| authorships[2].author.display_name | Rafał A. Bachorz |
| authorships[2].countries | PL |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210124861, https://openalex.org/I99542240 |
| authorships[2].affiliations[0].raw_affiliation_string | Institute of Medical Biology of Polish Academy of Sciences, Lodowa 106, 93-232 Lodz, Poland |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I46597724 |
| authorships[2].affiliations[1].raw_affiliation_string | Institute of Computing Science, Faculty of Computing, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland |
| authorships[2].institutions[0].id | https://openalex.org/I4210124861 |
| authorships[2].institutions[0].ror | https://ror.org/02kstzn75 |
| authorships[2].institutions[0].type | facility |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210124861, https://openalex.org/I99542240 |
| authorships[2].institutions[0].country_code | PL |
| authorships[2].institutions[0].display_name | Institute for Medical Biology |
| authorships[2].institutions[1].id | https://openalex.org/I99542240 |
| authorships[2].institutions[1].ror | https://ror.org/01dr6c206 |
| authorships[2].institutions[1].type | government |
| authorships[2].institutions[1].lineage | https://openalex.org/I99542240 |
| authorships[2].institutions[1].country_code | PL |
| authorships[2].institutions[1].display_name | Polish Academy of Sciences |
| authorships[2].institutions[2].id | https://openalex.org/I46597724 |
| authorships[2].institutions[2].ror | https://ror.org/00p7p3302 |
| authorships[2].institutions[2].type | education |
| authorships[2].institutions[2].lineage | https://openalex.org/I46597724 |
| authorships[2].institutions[2].country_code | PL |
| authorships[2].institutions[2].display_name | Poznań University of Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Rafał Adam Bachorz |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Institute of Computing Science, Faculty of Computing, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland, Institute of Medical Biology of Polish Academy of Sciences, Lodowa 106, 93-232 Lodz, Poland |
| authorships[3].author.id | https://openalex.org/A5024886465 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-1729-977X |
| authorships[3].author.display_name | Marcin Hoffmann |
| authorships[3].countries | PL |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I59411706 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Quantum Chemistry, Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland |
| authorships[3].institutions[0].id | https://openalex.org/I59411706 |
| authorships[3].institutions[0].ror | https://ror.org/04g6bbq64 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I59411706 |
| authorships[3].institutions[0].country_code | PL |
| authorships[3].institutions[0].display_name | Adam Mickiewicz University in Poznań |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Marcin Hoffmann |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | Department of Quantum Chemistry, Faculty of Chemistry, Adam Mickiewicz University in Poznan, Uniwersytetu Poznanskiego 8, 61-614 Poznan, Poland |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1424-8247/17/2/173/pdf?version=1706545606 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Machine Learning Application for Medicinal Chemistry: Colchicine Case, New Structures, and Anticancer Activity Prediction |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10211 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1703 |
| primary_topic.subfield.display_name | Computational Theory and Mathematics |
| primary_topic.display_name | Computational Drug Discovery Methods |
| related_works | https://openalex.org/W2382340815, https://openalex.org/W4254333899, https://openalex.org/W1972650408, https://openalex.org/W2409494250, https://openalex.org/W4290174252, https://openalex.org/W4366990902, https://openalex.org/W4317732970, https://openalex.org/W4388550696, https://openalex.org/W4321636153, https://openalex.org/W4313289487 |
| cited_by_count | 6 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 6 |
| locations_count | 4 |
| best_oa_location.id | doi:10.3390/ph17020173 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S16322639 |
| best_oa_location.source.issn | 1424-8247 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1424-8247 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Pharmaceuticals |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1424-8247/17/2/173/pdf?version=1706545606 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Pharmaceuticals |
| best_oa_location.landing_page_url | https://doi.org/10.3390/ph17020173 |
| primary_location.id | doi:10.3390/ph17020173 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S16322639 |
| primary_location.source.issn | 1424-8247 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1424-8247 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Pharmaceuticals |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1424-8247/17/2/173/pdf?version=1706545606 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Pharmaceuticals |
| primary_location.landing_page_url | https://doi.org/10.3390/ph17020173 |
| publication_date | 2024-01-29 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4297796727, https://openalex.org/W3025593963, https://openalex.org/W2578240541, https://openalex.org/W4316464459, https://openalex.org/W4361218849, https://openalex.org/W4382631609, https://openalex.org/W4388158637, https://openalex.org/W2791355014, https://openalex.org/W1975147762, https://openalex.org/W4384203178, https://openalex.org/W2566077590, https://openalex.org/W2471711949, https://openalex.org/W2078314973, https://openalex.org/W2016505381, https://openalex.org/W2041516742, https://openalex.org/W2591159150, https://openalex.org/W2105199186, https://openalex.org/W3120865887, https://openalex.org/W4288400169, https://openalex.org/W3009740019, https://openalex.org/W3046834349, https://openalex.org/W3204886005, https://openalex.org/W3204807958, https://openalex.org/W3165987353, https://openalex.org/W4307468223, https://openalex.org/W2800705832, https://openalex.org/W2068212264, https://openalex.org/W1967320885, https://openalex.org/W2270330859, https://openalex.org/W3102476541, https://openalex.org/W3005505802, https://openalex.org/W4229447215, https://openalex.org/W2134967712, https://openalex.org/W2997160604, https://openalex.org/W2120898782, https://openalex.org/W2064571598, https://openalex.org/W3030402150, https://openalex.org/W2412446857, https://openalex.org/W2169678694, https://openalex.org/W4233231723, https://openalex.org/W2102148524, https://openalex.org/W3182706339, https://openalex.org/W2981027525, https://openalex.org/W3087065520, https://openalex.org/W1551500840, https://openalex.org/W4256567637, https://openalex.org/W1983478747, https://openalex.org/W1972935024, https://openalex.org/W4387077712, https://openalex.org/W6929377222, https://openalex.org/W2105668062, https://openalex.org/W2610550630, https://openalex.org/W3016296419, https://openalex.org/W4225289163, https://openalex.org/W2043212464, https://openalex.org/W2151256267, https://openalex.org/W2167549547, https://openalex.org/W2218526232, https://openalex.org/W4385381094, https://openalex.org/W1516964807, https://openalex.org/W4220998614, https://openalex.org/W2187089797, https://openalex.org/W4243367687, https://openalex.org/W2132629607, https://openalex.org/W2295598076, https://openalex.org/W2042416054, https://openalex.org/W4200534198, https://openalex.org/W2898761039, https://openalex.org/W1930624869, https://openalex.org/W1579838312 |
| referenced_works_count | 70 |
| abstract_inverted_index.a | 107, 128, 202, 221 |
| abstract_inverted_index.In | 0 |
| abstract_inverted_index.ML | 55, 130 |
| abstract_inverted_index.We | 126 |
| abstract_inverted_index.an | 210 |
| abstract_inverted_index.as | 62, 124, 179 |
| abstract_inverted_index.at | 133 |
| abstract_inverted_index.by | 64, 86, 181 |
| abstract_inverted_index.in | 22, 58, 217 |
| abstract_inverted_index.is | 15, 78, 100 |
| abstract_inverted_index.of | 6, 31, 40, 106, 148, 191, 201, 220, 226 |
| abstract_inverted_index.on | 139, 232 |
| abstract_inverted_index.to | 18, 35, 53, 80, 90, 102, 109, 117, 186 |
| abstract_inverted_index.Our | 143 |
| abstract_inverted_index.The | 28, 74, 175 |
| abstract_inverted_index.and | 14, 171 |
| abstract_inverted_index.has | 10 |
| abstract_inverted_index.its | 118 |
| abstract_inverted_index.new | 205 |
| abstract_inverted_index.the | 1, 4, 37, 65, 82, 96, 104, 146, 188, 199, 218 |
| abstract_inverted_index.was | 34, 184 |
| abstract_inverted_index.(ML) | 9 |
| abstract_inverted_index.(RI) | 77 |
| abstract_inverted_index.(SI) | 99 |
| abstract_inverted_index.IC50 | 66, 211, 229 |
| abstract_inverted_index.LoVo | 91 |
| abstract_inverted_index.This | 48, 195 |
| abstract_inverted_index.cell | 46, 93, 153 |
| abstract_inverted_index.drug | 83 |
| abstract_inverted_index.each | 208 |
| abstract_inverted_index.era, | 3 |
| abstract_inverted_index.five | 44, 152 |
| abstract_inverted_index.most | 176 |
| abstract_inverted_index.such | 123 |
| abstract_inverted_index.this | 32, 214 |
| abstract_inverted_index.adept | 132 |
| abstract_inverted_index.based | 231 |
| abstract_inverted_index.being | 16 |
| abstract_inverted_index.cells | 88, 115 |
| abstract_inverted_index.index | 76, 98 |
| abstract_inverted_index.known | 140 |
| abstract_inverted_index.novel | 129, 135, 192 |
| abstract_inverted_index.study | 215 |
| abstract_inverted_index.tree, | 165 |
| abstract_inverted_index.tumor | 114 |
| abstract_inverted_index.while | 68 |
| abstract_inverted_index.(QSAR) | 26 |
| abstract_inverted_index.across | 43, 151 |
| abstract_inverted_index.assess | 36 |
| abstract_inverted_index.cancer | 92 |
| abstract_inverted_index.cells, | 122 |
| abstract_inverted_index.gained | 11 |
| abstract_inverted_index.input. | 235 |
| abstract_inverted_index.linear | 173 |
| abstract_inverted_index.lines, | 154 |
| abstract_inverted_index.lines. | 47, 94 |
| abstract_inverted_index.model, | 178, 224 |
| abstract_inverted_index.models | 56, 157 |
| abstract_inverted_index.normal | 121 |
| abstract_inverted_index.random | 162 |
| abstract_inverted_index.sought | 52 |
| abstract_inverted_index.system | 131 |
| abstract_inverted_index.value, | 67 |
| abstract_inverted_index.value. | 212 |
| abstract_inverted_index.values | 230 |
| abstract_inverted_index.vector | 167 |
| abstract_inverted_index.LoVo/DX | 87 |
| abstract_inverted_index.against | 113, 120 |
| abstract_inverted_index.augment | 19 |
| abstract_inverted_index.capable | 225 |
| abstract_inverted_index.forest, | 163 |
| abstract_inverted_index.library | 203 |
| abstract_inverted_index.machine | 7 |
| abstract_inverted_index.predict | 187 |
| abstract_inverted_index.quality | 182 |
| abstract_inverted_index.support | 166 |
| abstract_inverted_index.various | 159 |
| abstract_inverted_index.yielded | 198 |
| abstract_inverted_index.activity | 61, 190 |
| abstract_inverted_index.approach | 197 |
| abstract_inverted_index.assigned | 209 |
| abstract_inverted_index.chemical | 136 |
| abstract_inverted_index.compared | 116 |
| abstract_inverted_index.compound | 108 |
| abstract_inverted_index.computed | 79, 101 |
| abstract_inverted_index.decision | 164 |
| abstract_inverted_index.distinct | 45 |
| abstract_inverted_index.efficacy | 112 |
| abstract_inverted_index.employed | 185 |
| abstract_inverted_index.endeavor | 50 |
| abstract_inverted_index.entailed | 145 |
| abstract_inverted_index.evaluate | 81 |
| abstract_inverted_index.learning | 8 |
| abstract_inverted_index.metrics, | 183 |
| abstract_inverted_index.multiple | 172 |
| abstract_inverted_index.relative | 89 |
| abstract_inverted_index.research | 33, 49 |
| abstract_inverted_index.resulted | 216 |
| abstract_inverted_index.superior | 111 |
| abstract_inverted_index.toxicity | 119 |
| abstract_inverted_index.BALB/3T3. | 125 |
| abstract_inverted_index.activity. | 142 |
| abstract_inverted_index.attention | 13 |
| abstract_inverted_index.compounds | 42 |
| abstract_inverted_index.construct | 54 |
| abstract_inverted_index.determine | 103 |
| abstract_inverted_index.employing | 155 |
| abstract_inverted_index.exhibited | 85 |
| abstract_inverted_index.including | 161 |
| abstract_inverted_index.introduce | 127 |
| abstract_inverted_index.k-nearest | 169 |
| abstract_inverted_index.leveraged | 17 |
| abstract_inverted_index.machines, | 168 |
| abstract_inverted_index.molecular | 233 |
| abstract_inverted_index.objective | 30 |
| abstract_inverted_index.potential | 39, 105 |
| abstract_inverted_index.principal | 29 |
| abstract_inverted_index.structure | 234 |
| abstract_inverted_index.utilizing | 158 |
| abstract_inverted_index.validated | 222 |
| abstract_inverted_index.Meanwhile, | 95 |
| abstract_inverted_index.anticancer | 38, 60, 141, 189 |
| abstract_inverted_index.assessment | 147 |
| abstract_inverted_index.compounds, | 207 |
| abstract_inverted_index.compounds. | 73, 194 |
| abstract_inverted_index.determined | 180 |
| abstract_inverted_index.estimating | 228 |
| abstract_inverted_index.generating | 70 |
| abstract_inverted_index.inhibitory | 149 |
| abstract_inverted_index.innovative | 71 |
| abstract_inverted_index.neighbors, | 170 |
| abstract_inverted_index.predicated | 138 |
| abstract_inverted_index.predictive | 156, 223 |
| abstract_inverted_index.proficient | 57, 177 |
| abstract_inverted_index.quantified | 63 |
| abstract_inverted_index.reasonably | 227 |
| abstract_inverted_index.resistance | 75, 84 |
| abstract_inverted_index.structures | 137 |
| abstract_inverted_index.ultimately | 51 |
| abstract_inverted_index.widespread | 12 |
| abstract_inverted_index.algorithms, | 160 |
| abstract_inverted_index.demonstrate | 110 |
| abstract_inverted_index.development | 219 |
| abstract_inverted_index.exploration | 5 |
| abstract_inverted_index.forecasting | 59 |
| abstract_inverted_index.regression. | 174 |
| abstract_inverted_index.selectivity | 97 |
| abstract_inverted_index.traditional | 20 |
| abstract_inverted_index.capabilities | 150 |
| abstract_inverted_index.concurrently | 69 |
| abstract_inverted_index.contemporary | 2 |
| abstract_inverted_index.encompassing | 204 |
| abstract_inverted_index.quantitative | 23 |
| abstract_inverted_index.recommending | 134 |
| abstract_inverted_index.relationship | 25 |
| abstract_inverted_index.Additionally, | 213 |
| abstract_inverted_index.establishment | 200 |
| abstract_inverted_index.investigation | 144 |
| abstract_inverted_index.methodologies | 21 |
| abstract_inverted_index.methodological | 196 |
| abstract_inverted_index.investigations. | 27 |
| abstract_inverted_index.colchicine-based | 41, 193, 206 |
| abstract_inverted_index.colchicine-derived | 72 |
| abstract_inverted_index.structure–activity | 24 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5024886465, https://openalex.org/A5046613987 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I59411706 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.90956034 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |