Time-dependent AI-Modeling of the anticancer efficacy of synthesized gallic acid analogues.

Journal: Computational biology and chemistry

PMID: 30818108

Abstract

BACKGROUND/AIM: Main objective of this study is mapping of the anticancer efficacy of synthesized gallic acid analogues using modeling and artificial intelligence (AI) over a large range of concentrations and exposure times to explore the underline mechanisms of drug action and draw careful inferences regarding drug response heterogeneity.

Authors

Lubna Sherin

Department of Chemistry, Comsats University Islamabad, Lahore Campus, 54000, Pakistan. Electronic address: lubnasherin@cuilahore.edu.pk.
Ayesha Sohail

Department of Mathematics, Comsats University Islamabad, Lahore Campus, 54000, Pakistan. Electronic address: ayeshasohail81@gmail.com.
Shahida Shujaat

Department of Chemistry, Lahore College for Women University Lahore, 54000, Pakistan.

Keywords

Algorithms Antineoplastic Agents Artificial Intelligence Cell Line, Tumor Cell Proliferation Dose-Response Relationship, Drug Drug Screening Assays, Antitumor Gallic Acid Humans Machine Learning Models, Molecular Molecular Structure PC-3 Cells Structure-Activity Relationship Support Vector Machine Time Factors

External Resources

View on PubMed Access via DOI PubMed (30818108)

Time-dependent AI-Modeling of the anticancer efficacy of synthesized gallic acid analogues.

Abstract

Authors

Keywords

External Resources

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