AIMC Topic: Antimalarials
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Predicting Antimalarial Activity in Natural Products Using Pretrained Bidirectional Encoder Representations from Transformers.
Journal of chemical information and modeling
Aug 16, 2021
Malaria is a threatening disease that has claimed many lives and has a high prevalence rate annually. Through the past decade, there have been many studies to uncover effective antimalarial compounds to combat this disease. Alongside chemically synth...
Artificial Intelligence Applied to the Rapid Identification of New Antimalarial Candidates with Dual-Stage Activity.
ChemMedChem
Mar 16, 2021
Increasing reports of multidrug-resistant malaria parasites urge the discovery of new effective drugs with different chemical scaffolds. Protein kinases play a key role in many cellular processes such as signal transduction and cell division, making ...
Deep Learning-driven research for drug discovery: Tackling Malaria.
PLoS computational biology
Feb 18, 2020
Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is ...
Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error.
Trials
Feb 10, 2020
BACKGROUND: Retrospective exploratory analyses of randomised controlled trials (RCTs) seeking to identify treatment effect heterogeneity (TEH) are prone to bias and false positives. Yet the desire to learn all we can from exhaustive data measurements...
Discovery of potential 1,3,5-Triazine compounds against strains of Plasmodium falciparum using supervised machine learning models.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Dec 25, 2019
The Malaria burden was an escalating global encumbrance and need to be addressed with critical care. Anti-malarial drug discovery was integrated with supervised machine learning (ML) models to identify potent thiazolyl-traizine derivatives. This assi...
Development and rigorous validation of antimalarial predictive models using machine learning approaches.
SAR and QSAR in environmental research
Jul 22, 2019
The large collection of known and experimentally verified compounds from the ChEMBL database was used to build different classification models for predicting the antimalarial activity against . Four different machine learning methods, namely the supp...
Application of molecular docking and PSO-SVR intelligent approaches in antimalarial activity prediction of enantiomeric cycloguanil analogues.
SAR and QSAR in environmental research
Nov 1, 2018
A series of antifolate compounds, i.e. 1-(4-chlorophenyl)-6,6-dimethyl-1,3,5-triazine-2,4-diamine, or cycloguanil analogues, have shown effective inhibiting properties against Plasmodium falciparum dihydrofolate reductase (PfDHFR). In this work, the ...
Tetrahydrofolate Dehydrogenase
Substrate Specificity
Triazines
Proguanil
Quantitative Structure-Activity Relationship
Stereoisomerism
Folic Acid Antagonists
Reproducibility of Results
Molecular Docking Simulation
Machine Learning
Protein Binding
Algorithms
Antimalarials
Plasmodium falciparum
Enzyme Inhibitors
Molecular Structure
Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach.
PloS one
Sep 28, 2018
In view of the vast number of natural products with potential antiplasmodial bioactivity and cost of conducting antiplasmodial bioactivity assays, it may be judicious to learn from previous antiplasmodial bioassays and predict bioactivity of these na...
A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria.
Malaria journal
Apr 11, 2018
BACKGROUND: Nearly half of the world's population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identif...
Machine learning prioritizes synthesis of primaquine ureidoamides with high antimalarial activity and attenuated cytotoxicity.
European journal of medicinal chemistry
Jan 31, 2018
Primaquine (PQ) is a commonly used drug that can prevent the transmission of Plasmodium falciparum malaria, however toxicity limits its use. We prepared five groups of PQ derivatives: amides 1a-k, ureas 2a-k, semicarbazides 3a,b, acylsemicarbazides 4...
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