AIMC Topic: Antiprotozoal Agents

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Antileishmanial activity of novel indolyl-coumarin hybrids: Design, synthesis, biological evaluation, molecular docking study and in silico ADME prediction.

Bioorganic & medicinal chemistry letters Dec 24, 2015
In present work we have designed and synthesized total twelve novel 3-(3-(1H-indol-3-yl)-3-phenylpropanoyl)-4-hydroxy-2H-chromen-2-one derivatives 13(a-l) using Ho(3+) doped CoFe2O4 nanoparticles as catalyst and evaluated for their potential antileis...
Cell Survival Oxidoreductases Protozoan Proteins Structure-Activity Relationship Protein Structure, Tertiary HeLa Cells Coumarins Binding Sites Inhibitory Concentration 50 Leishmania Enzyme Inhibitors Humans Antiprotozoal Agents Molecular Docking Simulation Drug Design Antioxidants
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Application of Machine Learning (ML) approach in discovery of novel drug targets against Leishmania: A computational based approach.

Computational biology and chemistry Aug 1, 2025
Molecules with potent anti-leishmanial activity play a crucial role in identifying treatments for leishmaniasis and aiding in the design of novel drugs to combat the disease, ultimately protecting individuals and populations. Various methods have bee...
Humans Antiprotozoal Agents Machine Learning Leishmaniasis Drug Discovery Leishmania
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Computational Identification of Chemical Compounds with Potential Activity against Leishmania amazonensis using Nonlinear Machine Learning Techniques.

Current topics in medicinal chemistry Jan 1, 2018
Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including to...
Leishmania Machine Learning Antiprotozoal Agents Software Parasitic Sensitivity Tests Models, Molecular Drug Evaluation, Preclinical
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Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.

SAR and QSAR in environmental research Sep 1, 2017
The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to ...
Neural Networks, Computer Machine Learning Quantitative Structure-Activity Relationship Tetrahymena pyriformis Phenols Antiprotozoal Agents
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Role of moving average analysis for development of multi-target (Q)SAR models.

Mini reviews in medicinal chemistry Jan 1, 2015
In modern drug discovery era, multi target- quantitative structure activity relationship [mt- (Q)SAR] approaches have emerged as novel and powerful alternatives in the field of in-silico drug design so as to facilitate the discovery of new chemical e...
Plasmodium falciparum Models, Biological Malaria, Falciparum Antiprotozoal Agents Trypanosomiasis, African Computer Simulation Trypanosoma brucei rhodesiense Quantitative Structure-Activity Relationship Drug Design Humans Benzyl Compounds Artificial Intelligence
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In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect.

Mini reviews in medicinal chemistry Jan 1, 2015
The assessment of acute toxicity is one of the most important stages to ensure the safety of chemicals with potential applications in pharmaceutical sciences, biomedical research, or any other industrial branch. A huge and indiscriminate number of to...
Vitamin B Complex Databases, Pharmaceutical Benzylamines Heterocyclic Compounds Toxicological Phenomena Cyclams Fullerenes Thiamine Antiprotozoal Agents Tinidazole Humans Pharmaceutical Preparations Quantitative Structure-Activity Relationship Neural Networks, Computer Models, Biological Animals Anti-HIV Agents Drug Discovery Models, Molecular Computer Simulation
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