ArticleBiomolecules2018
In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs.
Article in Biomolecules, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 41 citations in OpenAlex.
- Assessment of quantum chemical predictors for anti-colorectal cancer agents using QSAR modeling.Journal of computer-aided molecular design · 2026Article
- 3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds.Journal of computer-aided molecular design · 2025Article
- DPPPRED-IV: An Ensembled QSAR-Based Web Server for the Prediction of Dipeptidyl Peptidase 4 Inhibitors.International journal of molecular sciences · 2025Article
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- Investigations of In Vitro Anti-Acetylcholinesterase, Anti-Diabetic, Anti-Inflammatory, and Anti-Cancer Efficacy of Garden Cress (Pharmaceutics · 2025Article
- Green one-pot synthesis of quinoxaline derivatives using sulfo-anthranilic acid functionalized alginate-MCFeRSC advances · 2025Article
- PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning.Journal of natural products · 2024Article
- Review
- Review
- Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer.Exploration of targeted anti-tumor therapy · 2023Review
- Halophyte Plants as Potential Sources of Anticancer Agents: A Comprehensive Review.Pharmaceutics · 2022Review
- Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer.Current oncology (Toronto, Ont.) · 2022Review
- Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach.Marine drugs · 2022Article
- Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification.Frontiers in oncology · 2022Review
- Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era.Current oncology (Toronto, Ont.) · 2021Review
- A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition.Marine drugs · 2020Article
- Application of artificial intelligence to the diagnosis and therapy of colorectal cancer.American journal of cancer research · 2020Review
- Machine Learning for Molecular Modelling in Drug Design.Biomolecules · 2019Article
- Exploiting machine learning for end-to-end drug discovery and development.Nature materials · 2019Article
- A Computer-Driven Approach to Discover Natural Product Leads for Methicillin-ResistantMarine drugs · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure⁻activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC
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What OpenQuestion holds
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.