ArticleComputational and structural biotechnology journal2024
NaCTR: Natural product-derived compound-based drug discovery pipeline from traditional oriental medicine by search space reduction.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Nature meets machine: the AI renaissance in natural product drug discovery.Natural products and bioprospecting · 2026Review
- Bicuculline fromComputational and structural biotechnology journal · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The drug discovery pipelines require enormous time and cost, albeit their infamously high risk of failures. Reducing such risk has therefore been the utmost goal in the process. Recently, natural products (NPs) in traditional oriental medicine (TOM) have come into the spotlight for their efficacy and safety supported throughout the history. Not only that, with the ever-increasing repository of various biological datasets, many data-driven
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.