ArticlePharmaceuticals (Basel, Switzerland)2020
Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds Against
Article in Pharmaceuticals (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Pharmacokinetic Equations Applied to Obtain New Topological Models in the Search of Antibacterial Compounds.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Drug repurposing targeting COVID-19 3CL protease using molecular docking and machine learning regression approaches.Scientific reports · 2025Article
- Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery.Journal of xenobiotics · 2024Review
- Potential Repurposed Drug Candidates for Tuberculosis Treatment: Progress and Update of Drugs Identified in Over a Decade.ACS omega · 2023Review
- Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design.International journal of molecular sciences · 2022Review
- New Pharmacokinetic and Microbiological Prediction Equations to Be Used as Models for the Search of Antibacterial Drugs.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Machine Learning in Antibacterial Drug Design.Frontiers in pharmacology · 2022Review
- Molecular Topology for the Search of New Anti-MRSA Compounds.International journal of molecular sciences · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their values for said index in order to construct probability spaces. The second step consists in the calculation of a discriminant function which determines the prediction of the model. The model was used to screen the DrugBank database, identifying 134 drugs as possible antibacterial candidates. Out of these 134 drugs, 8 were antibacterial drugs, 67 were drugs approved for different pathologies and 55 were drugs in experimental stages. This methodology has proven to be a viable alternative to the traditional methods used to obtain prediction models based on LDA and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments. Furthermore, the topological indexes
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