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Record W4399494815 · doi:10.1002/cmdc.202400316

Design and Synthesis of Novel β‐Carboline‐Bisindole Hybrids as Potential Anticancer Agents

2024· article· en· W4399494815 on OpenAlexaff
Nguyễn Thị Thanh Huyền, Ban Van Phuc, Huyen Thi Thanh Tran, Tran Thi Hong, Hien Nguyen, Van Ha Nguyen, Minh Tho Nguyen, Tran Quang Hung, Chau Phi Dinh

Bibliographic record

VenueChemMedChem · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSynthesis and bioactivity of alkaloids
Canadian institutionsDiscovery Air (Canada)
FundersQuỹ Đổi mới sáng tạo VingroupTập đoàn Vingroup - Công ty CP
KeywordsCombinatorial chemistryChemistryCancer cell linesTryptamineCancer cellComputational biologyStereochemistryCancerBiochemistryBiology

Abstract

fetched live from OpenAlex

Abstract We are reporting a short and convenient pathway for the synthesis of novel β‐carboline‐bisindole hybrid compounds from relatively cheap and commercially available chemicals such as tryptamine, dialdehydes and indoles. These newly designed compounds can also be prepared in high yields with the tolerance of many functional groups under mild conditions. Notably, these β‐carboline‐bisindole hybrid compounds exhibited some promising applications as anticancer agents against the three common cancer cell lines MCF‐7 (breast cancer), SK‐LU‐1 (lung cancer), and HepG2 (liver cancer). The two best compounds 5 b and 5 g inhibited the aforementioned cell lines with the same IC50 range of the reference Ellipticine at less than 2 μM. A molecular docking study to gain more information about the interactions between the synthesized molecules and the kinase domain of the EGFR was performed. Therefore, this finding can have significant impacts on the development of future research in medicinal chemistry and drug discovery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.267
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueChemMedChemSame topicSynthesis and bioactivity of alkaloidsFrench-language works237,207