Discovering New Agents Active against Methicillin-Resistant <i>Staphylococcus aureus</i> with Ligand-Based Approaches

Le, Xiu; Li, Long; Ju, Yingchen; Lin, Zhenwu; Gu, Qiong; Xu, Jing

doi:10.1021/ci500253q

Cited by 43 publications

(43 citation statements)

References 46 publications

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“…Recently, Ebejer et al [24] reported the comparison between the physicochemical properties of antibacterial compounds and other drugs and concluded that the percentage of compounds with TopoPSA < 120 Å 2 is 84.4% and 52.6% for marketed other drugs and antibacterial drugs, respectively, which is in agreement with our previously stablished rule [21]. Wang et al developed in silico classification models derived from 5451 cell-based anti-MRSA assay data using four machine learning (ML) methods, including naïve Bayesian, support vector machine (SVM), recursive partitioning, and k -nearest neighbors [25]. The best model was employed for the virtual screening of anti-MRSA compounds, which were validated by cell-based assays with three types of highly resistant MRSA strains and a total of 12 new anti-MRSA agents were experimentally confirmed by the authors [25].…”

Section: Introductionsupporting

confidence: 78%

“…Following our previous work that modeling the anticancer activity against HCT116 [37], the current results suggest as well that the chemoinformatics QSAR approach relying on a ligand-based methodology either based on the molecular structures or the NMR spectra, corroborated with an experimental approach, could be used to predict new inhibitory compounds against MRSA. To our knowledge, the QSAR regression model developed here, approach A, is the largest study ever performed with regard both to the number of compounds involved and to the number of structural families involved in the modeling of the antibacterial activity against MRSA [19,20,25]. The NMR QSAR classification model, approach B, was extended to a high number of samples containing additional 45 pure compounds and therefore the overall predictability accuracies (Q) were improved as compared with those obtained in our previously work [37].…”

Section: Discussionmentioning

confidence: 99%

“…Wang et al developed in silico classification models derived from 5451 cell-based anti-MRSA assay data using four machine learning (ML) methods, including naïve Bayesian, support vector machine (SVM), recursive partitioning, and k -nearest neighbors [25]. The best model was employed for the virtual screening of anti-MRSA compounds, which were validated by cell-based assays with three types of highly resistant MRSA strains and a total of 12 new anti-MRSA agents were experimentally confirmed by the authors [25].…”

Section: Introductionmentioning

confidence: 99%

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A Computer-Driven Approach to Discover Natural Product Leads for Methicillin-Resistant Staphylococcus aureus Infection Therapy

2018

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The risk of methicillin-resistant Staphylococcus aureus (MRSA) infection is increasing in both the developed and developing countries. New approaches to overcome this problem are in need. A ligand-based strategy to discover new inhibiting agents against MRSA infection was built through exploration of machine learning techniques. This strategy is based in two quantitative structure–activity relationship (QSAR) studies, one using molecular descriptors (approach A) and the other using descriptors (approach B). In the approach A, regression models were developed using a total of 6645 molecules that were extracted from the ChEMBL, PubChem and ZINC databases, and recent literature. The performance of the regression models was successfully evaluated by internal and external validation, the best model achieved R2 of 0.68 and RMSE of 0.59 for the test set. In general natural product (NP) drug discovery is a time-consuming process and several strategies for dereplication have been developed to overcome this inherent limitation. In the approach B, we developed a new NP drug discovery methodology that consists in frontloading samples with 1D NMR descriptors to predict compounds with antibacterial activity prior to bioactivity screening for NPs discovery. The NMR QSAR classification models were built using 1D NMR data (1H and 13C) as descriptors, from crude extracts, fractions and pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 77% for both training and test sets.

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Section: Introductionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Computer-Driven Approach to Discover Natural Product Leads for Methicillin-Resistant Staphylococcus aureus Infection Therapy

2018

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“…Wang et al chose an NB classifier from over 800 ML models that utilized four distinct techniques because it performed the best on external testing and gave favorable fragments. When using this classifier on a novel compound database, 56 hits were found after filtering, and in vitro assays deemed 12 of them as significantly active [42]. Jang et al used an NB classifier as a crucial step in their drug discovery workflow, which produced new and structurally diverse hits for mGlu1 receptor inhibitors [21].…”

Section: Machine Learning Methods For Virtual Screeningmentioning

confidence: 99%

Machine Learning-based Virtual Screening and Its Applications to Alzheimer’s Drug Discovery: A Review

Carpenter

Huang

2018

CPD

149

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Background: Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increas-ing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered train-ing set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity.Objective: The study aims to review ML-based methods used for VS and applications to Alzheimer’s Disease (AD) drug discovery.Methods: To update the current knowledge on ML for VS, we review thorough backgrounds, explana-tions, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN).Results: All techniques have found success in VS, but the future of VS is likely to lean more largely toward the use of neural networks – and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for con-ducting ML-based VS for potential therapeutics for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation.Conclusion: Different ML techniques are powerful tools for VS, and they have advantages and disad-vantages albeit. ML-based VS can be applied to AD drug development.

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“…Student's t-test was used to calculate the p-value of all the properties of active and inactive compounds. 32 The p-value and the correlation coefficients between the a NLG8189 was used as the positive control. properties and inhibitory activity were calculated using SPSS 13.0.…”

Section: Diversity Analysismentioning

confidence: 99%

Discovery of indoleamine 2,3-dioxygenase inhibitors using machine learning based virtual screening

Zhang

Liu

et al. 2018

Med. Chem. Commun.

Self Cite

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Indoleamine 2,3-dioxygenase (IDO), an immune checkpoint, is a promising target for cancer immunotherapy. However, current IDO inhibitors are not approved for clinical use yet; therefore, new IDO inhibitors are still demanded. To identify new IDO inhibitors, we have built naive Bayesian (NB) and recursive partitioning (RP) models from a library of known IDO inhibitors derived from recent publications. Thirteen molecular fingerprints were used as descriptors for the models to predict IDO inhibitors. An in-house compound library was virtually screened using the best machine learning model, which resulted in 50 hits for further enzyme-based IDO inhibitory assays. Consequently, we identified three new IDO inhibitors with IC values of 1.30, 4.10, and 4.68 μM. These active compounds also showed IDO inhibitory activities in cell-based assays. The compounds belong to the tanshinone family, a typical scaffold family derived from Danshen (a Chinese herb), the dried root of , which has been widely used in China, Japan, the United States, and other European countries for the treatment of cardiovascular and cerebrovascular diseases. Thus, we discovered a new use for Danshen using machine learning methods. Surface plasmon resonance (SPR) experiments proved that the inhibitors interacted with the IDO target. Molecular dynamic simulations demonstrated the binding modes of the IDO inhibitors.

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Discovering New Agents Active against Methicillin-Resistant Staphylococcus aureus with Ligand-Based Approaches

Cited by 43 publications

References 46 publications

A Computer-Driven Approach to Discover Natural Product Leads for Methicillin-Resistant Staphylococcus aureus Infection Therapy

A Computer-Driven Approach to Discover Natural Product Leads for Methicillin-Resistant Staphylococcus aureus Infection Therapy

Machine Learning-based Virtual Screening and Its Applications to Alzheimer’s Drug Discovery: A Review

Discovery of indoleamine 2,3-dioxygenase inhibitors using machine learning based virtual screening

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