Cargando…

Privileged substructures for anti-sickling activity via cheminformatic analysis

Sickle cell disease (SCD), an autosomal recessive genetic disorder, has been recognized by the World Health Organization (WHO) as a major public health problem as it affects 300 000 individuals worldwide. Complications arising from SCD include anemia, microvascular occlusion, severe pain, stokes, re...

Descripción completa

Detalles Bibliográficos
Autores principales:	Phanus-umporn, Chuleeporn, Shoombuatong, Watshara, Prachayasittikul, Veda, Anuwongcharoen, Nuttapat, Nantasenamat, Chanin
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	The Royal Society of Chemistry 2018
Materias:	Chemistry
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9078244/ https://www.ncbi.nlm.nih.gov/pubmed/35539618 http://dx.doi.org/10.1039/c7ra12079f

Ejemplares similares

Correction: Privileged substructures for anti-sickling activity via cheminformatic analysis
por: Phanus-umporn, Chuleeporn, et al.
Publicado: (2018)

QSAR-driven rational design of novel DNA methyltransferase 1 inhibitors
por: Phanus-umporn, Chuleeporn, et al.
Publicado: (2020)

Navigating the chemical space of dipeptidyl peptidase-4 inhibitors
por: Shoombuatong, Watshara, et al.
Publicado: (2015)

Exploring the chemical space of influenza neuraminidase inhibitors
por: Anuwongcharoen, Nuttapat, et al.
Publicado: (2016)

Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking
por: Simeon, Saw, et al.
Publicado: (2016)

Prediction of aromatase inhibitory activity using the efficient linear method (ELM)
por: Shoombuatong, Watshara, et al.
Publicado: (2015)

osFP: a web server for predicting the oligomeric states of fluorescent proteins
por: Simeon, Saw, et al.
Publicado: (2016)

Classification of P-glycoprotein-interacting compounds using machine learning methods
por: Prachayasittikul, Veda, et al.
Publicado: (2015)

Cheminformatic Analysis and Machine Learning Modeling to Investigate Androgen Receptor Antagonists to Combat Prostate Cancer
por: Yu, Tianshi, et al.
Publicado: (2023)

Discovery of novel 1,2,3-triazole derivatives as anticancer agents using QSAR and in silico structural modification
por: Prachayasittikul, Veda, et al.
Publicado: (2015)

Exploring the Chemical Space of CYP17A1 Inhibitors Using Cheminformatics and Machine Learning
por: Yu, Tianshi, et al.
Publicado: (2023)

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides
por: Schaduangrat, Nalini, et al.
Publicado: (2019)

Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation
por: Schaduangrat, Nalini, et al.
Publicado: (2019)

TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides
por: Laengsri, Vishuda, et al.
Publicado: (2019)

Towards understanding aromatase inhibitory activity via QSAR modeling
por: Shoombuatong, Watshara, et al.
Publicado: (2018)

Origin of aromatase inhibitory activity via proteochemometric modeling
por: Simeon, Saw, et al.
Publicado: (2016)

DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists
por: Schaduangrat, Nalini, et al.
Publicado: (2023)

The MicroRNA Interaction Network of Lipid Diseases
por: Kandhro, Abdul H., et al.
Publicado: (2017)

Roles of d-Amino Acids on the Bioactivity of Host Defense Peptides
por: Li, Hao, et al.
Publicado: (2016)

Machine Learning Approaches to Investigate the Structure–Activity Relationship of Angiotensin-Converting Enzyme Inhibitors
por: Yu, Tianshi, et al.
Publicado: (2023)

Predicting Metabolic Syndrome Using the Random Forest Method
por: Worachartcheewan, Apilak, et al.
Publicado: (2015)

Rational design of novel sirtuin 1 activators via structure-activity insights from application of QSAR modeling
por: Pratiwi, Reny, et al.
Publicado: (2019)

Unraveling the bioactivity of anticancer peptides as deduced from machine learning
por: Shoombuatong, Watshara, et al.
Publicado: (2018)

Probing the origin of estrogen receptor alpha inhibition via large-scale QSAR study
por: Suvannang, Naravut, et al.
Publicado: (2018)

Machine learning approaches to study the structure-activity relationships of LpxC inhibitors
por: Yu, Tianshi, et al.
Publicado: (2023)

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties
por: Charoenkwan, Phasit, et al.
Publicado: (2019)

StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy
por: Schaduangrat, Nalini, et al.
Publicado: (2022)

Correction: Shoombuatong, W., et al. iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties. Int. J. Mol. Sci. 2020, 21, 75
por: Charoenkwan, Phasit, et al.
Publicado: (2020)

Towards combating antibiotic resistance by exploring the quantitative structure-activity relationship of NDM-1 inhibitors
por: Yu, Tianshi, et al.
Publicado: (2022)

Investigation of aromatase inhibitory activity of metal complexes of 8-hydroxyquinoline and uracil derivatives
por: Prachayasittikul, Veda, et al.
Publicado: (2014)

Large-scale comparative review and assessment of computational methods for phage virion proteins identification
por: Kabir, Muhammad, et al.
Publicado: (2022)

A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

Aromatase inhibitory activity of 1,4-naphthoquinone derivatives and QSAR study
por: Prachayasittikul, Veda, et al.
Publicado: (2017)

Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

Probing the origins of aromatase inhibitory activity of disubstituted coumarins via QSAR and molecular docking
por: Worachartcheewan, Apilak, et al.
Publicado: (2014)

SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids
por: Charoenkwan, Phasit, et al.
Publicado: (2022)

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning
por: Charoenkwan, Phasit, et al.
Publicado: (2022)

QSAR study of anti-prion activity of 2-aminothiazoles
por: Mandi, Prasit, et al.
Publicado: (2012)

Cannot write session to /tmp/vufind_sessions/sess_uopt1dpctogk81p9tt32j42l5h