Computer-Aided Classification of New Psychoactive Substances

Bărbulescu, Alina; Barbeș, Lucica; Dumitriu, Cristian Ștefan

doi:10.1155/2021/4816970

Cited by 5 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Topological polar surface area (TPSA) is an important parameter for estimating the properties of drug transporters, affecting Caco‐2 cell permeability, blood‐brain barrier penetration, and intestinal absorption [31] . According to the existing literature, most drugs have a TPSA value less than 120 Å [32] . In comparison with 9‐AA , the TPSA value of compound KY‐31 was upward, indicating poorer absorption (ABS) ( % ABS=109−(0.345×TPSA) [33] .…”

Section: Resultsmentioning

confidence: 99%

“…[31] According to the existing literature, most drugs have a TPSA value less than 120 Å. [32] In comparison with 9-AA, the TPSA value of compound KY-31 was upward, indicating poorer absorption (ABS) ( % ABS = 109À -(0.345×TPSA). [33] However, it remained compliant with 'Lipinski's rule of five', and most importantly it greatly reduced the toxicity of 9-AA.…”

Section: In Silico Adme Propertiesmentioning

confidence: 99%

See 1 more Smart Citation

Design, Synthesis, Biological Evaluation and Docking Study of A New Series of 9‐aminoacridine Derivatives as Anti‐inflammatory Agents

Zhao,

Song,

Shang

et al. 2024

ChemistrySelect

View full text Add to dashboard Cite

Abstract9‐aminacrine (9‐AA), an external antibacterial agent, possesses important bioactivities for the treatment of cancer, viruses, and prion diseases. Recent research has revealed that 9‐AA has anti‐inflammatory effects through the up‐regulation of nuclear receptor subfamily 4 group A member 1 (NR4A1), but it is highly cytotoxic. To mitigate its toxicity, three series of novel 9‐AA derivatives were synthesized. The toxicity of all compounds was assessed, and some of the less toxic compounds were tested for in vitro anti‐inflammatory activities, leading to the establishment of structure‐activity relationships. In particular, 9‐amino‐8‐bromo‐3,4‐dihydroacridin‐1(2H)‐one exhibited relatively low toxicity and retained anti‐inflammatory activity, with a higher selectivity index compared to 9‐AA (3.44 versus 0.68). Expression experiments of inflammatory mediators revealed that this compound not only inhibited the gene expression of pro‐inflammatory factors, but also promoted the gene expression of anti‐inflammatory factors. Preliminary mechanistic studies suggested that it exerted anti‐inflammatory effects through the NR4A1/Interleukin‐10 (IL‐10)/suppressors of cytokine signalling 3 (SOCS3) signaling pathway. Furthermore, molecular docking studies indicating that it may exhibit anti‐inflammatory activity by inhibiting the protein targets phosphodiesterase‐4 (PDE‐4) and p38 mitogen‐activated protein kinases (MAPK). Based on our data, 9‐amino‐8‐bromo‐3,4‐dihydroacridin‐1(2H)‐one with its low toxicity and anti‐inflammatory properties, holds promise for further development as an anti‐inflammatory agent.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: In Silico Adme Propertiesmentioning

confidence: 99%

Design, Synthesis, Biological Evaluation and Docking Study of A New Series of 9‐aminoacridine Derivatives as Anti‐inflammatory Agents

Zhao,

Song,

Shang

et al. 2024

ChemistrySelect

View full text Add to dashboard Cite

show abstract

“…Finding similarities of different molecules became a study field in the 90s [5], but they have been used mostly in drugs classification. Although many researchers investigated various chemical structures of antioxidants, UV stabilizers, or photoinitiators [3,[6][7][8][9][10][11] for polymeric materials, the classification of these molecules based on their components discovered using the new technologies attracted interest. Utilizing the capabilities of the latest software, this can be easily done.…”

Section: Introductionmentioning

confidence: 99%

Study of the additives for polymeric materials by cheminformatics methods

Bărbulescu

Barbeș

Dumitriu

2022

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

show abstract

From 2015 to 2023: How Machine Learning Aids Natural Product Analysis

Shi,

Huang,

et al. 2024

Chemistry Africa

View full text Add to dashboard Cite

In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity of and volume of data generated in contemporary research endeavors. Computational methodologies represent robust tools in the field of chemistry, offering the capacity to harness potent machine learning (ML) models to yield insightful analytical outcomes. This review examines the integration of machine learning into natural product chemistry from 2015 to 2023, highlighting its potential to overcome the inherent limitations of traditional chemical techniques. We present a structured approach that matches specific natural product challenges—such as component determination, concentration prediction, and classification—with suitable ML models, including regression, classification, and dimension reduction methods. Our objective is to illustrate how ML pipelines, from data preprocessing to model evaluation, enhance both qualitative and quantitative analyses, providing a comprehensive framework, with the potential catalyze a transformation in the field of natural product analysis.

show abstract

Computer-Aided Classification of New Psychoactive Substances

Cited by 5 publications

References 38 publications

Design, Synthesis, Biological Evaluation and Docking Study of A New Series of 9‐aminoacridine Derivatives as Anti‐inflammatory Agents

Design, Synthesis, Biological Evaluation and Docking Study of A New Series of 9‐aminoacridine Derivatives as Anti‐inflammatory Agents

Study of the additives for polymeric materials by cheminformatics methods

From 2015 to 2023: How Machine Learning Aids Natural Product Analysis

Contact Info

Product

Resources

About