QSAR Classification Models for Prediction of Hydroxamate Histone Deacetylase Inhibitor Activity against Malaria Parasites

Hesping, Eva; Chua, Ming Jang; Pflieger, Marc; Qian, Yunan; Dong, Lilong; Bachu, Prabhakar; Liu, Ligong; Kurz, Thomas; Fisher, Gillian; Skinner‐Adams, Tina S.; Reid, Robert C.; Fairlie, David P.; Andrews, Katherine Thea; Gorse, Alain-Dominique J.P.

doi:10.1021/acsinfecdis.1c00355

Cited by 9 publications

(3 citation statements)

References 66 publications

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“…In silico approaches, through molecular docking, also led to the discovery of a new portfolio of parasite specific HAT inhibitors [ 77 , 117 ] and quantitative structure–activity relationship (QSAR) models have been developed as useful tools for in silico screening of Plasmodium HDAC inhibitors [ 137 ]. However, recent progress has been made in the field of malaria epidrugs in order to overcome potential toxicity issues in the mammalian host.…”

Section: Plasmodium Epigenetic Effectors As a Source Of Ther...mentioning

confidence: 99%

Epigenetic regulation as a therapeutic target in the malaria parasite Plasmodium falciparum

Reyser,

Paloque,

Augereau

et al. 2024

Malar J

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Over the past thirty years, epigenetic regulation of gene expression has gained increasing interest as it was shown to be implicated in illnesses ranging from cancers to parasitic diseases. In the malaria parasite, epigenetics was shown to be involved in several key steps of the complex life cycle of Plasmodium, among which asexual development and sexual commitment, but also in major biological processes like immune evasion, response to environmental changes or DNA repair. Because epigenetics plays such paramount roles in the Plasmodium parasite, enzymes involved in these regulating pathways represent a reservoir of potential therapeutic targets. This review focuses on epigenetic regulatory processes and their effectors in the malaria parasite, as well as the inhibitors of epigenetic pathways and their potential as new anti-malarial drugs. Such types of drugs could be formidable tools that may contribute to malaria eradication in a context of widespread resistance to conventional anti-malarials.

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Section: Plasmodium Epigenetic Effectors As a Source Of Ther...mentioning

confidence: 99%

Epigenetic regulation as a therapeutic target in the malaria parasite Plasmodium falciparum

Reyser,

Paloque,

Augereau

et al. 2024

Malar J

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“…ACS Infectious Diseases is contributing to this joint virtual special issue focused on epigenetics with 11 publications in the form of nine innovative research articles, one Perspective, and one Viewpoint. Not surprisingly, histone deacetylase (HDAC) is the target class with a major presence (four research articles), as HDAC has already shown its therapeutic potential to treat infectious diseases. − Also notable is the increase of interest in bromodomains in protozoa; their potential as novel targets against protozoans-mediated diseases is discussed in a Viewpoint and two articles. , Finally, the characterization of acetyl-CoA synthetase as an interesting target for the development of antiplasmodial drugs, the profiling of epigenetic modulators against hemoprotozoan parasites, and two publications focused on the role of the host , complete the contributions from ACS Infectious Diseases to this virtual special issue. We thank Drs.…”

Section: Acs Infectious Diseasesmentioning

confidence: 99%

Virtual Special Issue: Epigenetics 2022

Aldrich¹,

Calderón²,

Conway³

et al. 2022

J. Med. Chem.

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“…In recent years, there have been several studies on the subject of QSAR analysis. These studies include research on the prediction of smallmolecule binding to RNA, predicting the transfer of environmental chemicals across the placenta, and the classification of inhibitor activity [9,10].…”

Section: Introductionmentioning

confidence: 99%

QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer's Disease Using Ensemble Machine Learning Algorithms

et al. 2023

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This study focuses on the development of a machine learning ensemble approach for the classification of Beta-Secretase 1 (BACE1) inhibitors in Quantitative Structure-Activity Relationship (QSAR) analysis. BACE1 is an enzyme linked to the production of amyloid beta peptide, a significant component of Alzheimer's disease plaques. The discovery of effective BACE1 inhibitors is difficult, but QSAR modeling offers a cost-effective alternative by predicting the activity of compounds based on their chemical structures. This study evaluates the performance of four machine learning models (Random Forest, AdaBoost, Gradient Boosting, and Extra Trees) in predicting BACE1 inhibitor activity. Random Forest achieved the highest performance, with a training accuracy of 98.65% and a testing accuracy of 82.53%. In addition, it exhibited superior precision, recall, and F1-score. Random Forest's superior performance was a result of its ability to capture a wide variety of patterns and its randomized ensemble approach. Overall, this study demonstrates the efficacy of ensemble machine learning models, specifically Random Forest, in predicting the activity of BACE1 inhibitors. The findings contribute to ongoing efforts in Alzheimer's disease drug discovery research by providing a cost-effective and efficient strategy for screening and prioritizing potential BACE1 inhibitors.

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QSAR Classification Models for Prediction of Hydroxamate Histone Deacetylase Inhibitor Activity against Malaria Parasites

Cited by 9 publications

References 66 publications

Epigenetic regulation as a therapeutic target in the malaria parasite Plasmodium falciparum

Epigenetic regulation as a therapeutic target in the malaria parasite Plasmodium falciparum

Virtual Special Issue: Epigenetics 2022

QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer's Disease Using Ensemble Machine Learning Algorithms

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