QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

Bugeac, Cosmin Alexandru; Ancuceanu, Robert; Dinu, Mihaela

doi:10.3390/molecules26061734

Cited by 11 publications

(5 citation statements)

References 93 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

Section: Methodsmentioning

confidence: 99%

“…In developing Quantitative Structure-Activity Relationship (QSAR) models, selecting molecular descriptors is of paramount importance [5]. These models’ effectiveness hinges on using unique and carefully chosen descriptors [5]. An optimal set of descriptors should capture the essential molecular characteristics relevant to the study while avoiding redundancy.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Unlocking Herbal Potentials: Novel Shikimate Kinase Inhibitors in the Fight Against Antibiotic Resistance

Nirgudkar,

Chai

2024

Preprint

View full text Add to dashboard Cite

Against a backdrop of stagnant antibiotic innovation, the escalating prevalence of antibiotic-resistant pathogens forecasts a challenging future [6]. Traditionally, antibiotics, predominantly derived from fungal sources, employ a limited set of mechanisms to inhibit bacterial growth [6, 16]. Shikimate Kinase has emerged as a promising antibacterial target due to its exclusivity to bacteria and the lethality of its inhibition [4, 13, 14, 15, 28, 29, 30]. Although synthetic inhibitors have been developed, the exploration of plant-derived alternatives remains untapped. Naturally derived plant-based compounds provide a more viable option because of the high cost of creating synthetic compounds. This study examines the Goldenrod plant, reputed in Native American Ethno-medicine for its antimicrobial properties [3, 12, 17]. Employing Liquid Chromatography - Mass Spectrometry (LC-MS) and Quantitative Structure Activity Relationship (QSAR) models, the study evaluates the plant’s compounds for their potential as antibacterial agents. Antibacterial activity againstBacillus Subtiliswas assessed using the Kirby-Bauer Disk Diffusion assay, and genetic sequencing was performed on mutants that overcame the initial inhibition zone. By comparing the parent and mutant strains, the mode of inhibition by the plant antibiotic was determined by backtracking. The study identified Shikimate Kinase as the inhibitory target of the plant-derived compounds. Molecular docking revealed a binding affinity of -8.9 kcal/mol for the most effective compound, which is statistically significant compared to Shikimate Acid, the enzyme’s natural substrate. Through Pymol visualization, competitive inhibition was confirmed, with the compound’s binding pocket exhibiting a druggability score of 0.84, approaching the threshold of clinical drugs. This research suggests new antibiotic classes targeting the Shikimate Kinase pathway, offering an alternative approach to tackling ESKAPE pathogens and enhancing health outcomes.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Unlocking Herbal Potentials: Novel Shikimate Kinase Inhibitors in the Fight Against Antibiotic Resistance

Nirgudkar,

Chai

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…A study developed multiple QSAR methods using several ML algorithms, including SVM, to predict the activity of active substances against Pseudomonas aeruginosa. The study found that SVM could better predict the compounds' activity accurately compared to other models [80]. Another study examined SVM's effectiveness and prognostication power in HEPT derivative QSAR modeling.…”

Section: Support Vector Machinementioning

confidence: 99%

Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review

Niazi

Mariam

2023

IJMS

View full text Add to dashboard Cite

In modern drug discovery, the combination of chemoinformatics and quantitative structure–activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure–activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

show abstract

“…The study found that SVM could better predict the compounds' activity among other models accurately. 77 Another study investigated SVM's performance and predictive capability in QSAR modeling of HEPT derivatives. This study compared SVM with other methods, such as artificial neural networks, and found that SVM achieved good predictive performance.…”

Section: Support Vector Machinementioning

confidence: 99%

Recent Advances in Machine Learning-Based Chemoinformatics: A Comprehensive Review

Niazi¹,

Mariam²

2023

Preprint

View full text Add to dashboard Cite

In the realm of modern drug discovery, the integration of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structural-activity relationships (SARs), play a pivotal role in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. By embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

show abstract

QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

Cited by 11 publications

References 93 publications

Unlocking Herbal Potentials: Novel Shikimate Kinase Inhibitors in the Fight Against Antibiotic Resistance

Unlocking Herbal Potentials: Novel Shikimate Kinase Inhibitors in the Fight Against Antibiotic Resistance

Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review

Recent Advances in Machine Learning-Based Chemoinformatics: A Comprehensive Review

Contact Info

Product

Resources

About