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Antimalarials

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Evaluating large language models as a supplementary patient information resource on antimalarial use in systemic lupus erythematosus.

Lupus
ObjectiveTo assess the accuracy, completeness, and reproducibility of Large Language Models (LLMs) (Copilot, GPT-3.5, and GPT-4) on antimalarial use in systemic lupus erythematosus (SLE).Materials and MethodsWe utilized 13 questions derived from pati...

Novel molecular inhibitor design for Plasmodium falciparum Lactate dehydrogenase enzyme using machine learning generated library of diverse compounds.

Molecular diversity
Generative machine learning models offer a novel strategy for chemogenomics and de novo drug design, allowing researchers to streamline their exploration of the chemical space and concentrate on specific regions of interest. In cases with limited inh...

MalariaFlow: A comprehensive deep learning platform for multistage phenotypic antimalarial drug discovery.

European journal of medicinal chemistry
Malaria remains a significant global health challenge due to the growing drug resistance of Plasmodium parasites and the failure to block transmission within human host. While machine learning (ML) and deep learning (DL) methods have shown promise in...

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery.

Journal of chemical information and modeling
Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to...

Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool.

Acta parasitologica
PURPOSE: Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation o...

MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites.

Genome biology
Malaria remains one of the deadliest infectious diseases. Transcriptional regulation effects of noncoding variants in this unusual genome of malaria parasites remain elusive. We developed a sequence-based, ab initio deep learning framework, MalariaSE...

Predicting Antimalarial Activity in Natural Products Using Pretrained Bidirectional Encoder Representations from Transformers.

Journal of chemical information and modeling
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...

In silico toxicity profiling of natural product compound libraries from African flora with anti-malarial and anti-HIV properties.

Computational biology and chemistry
This paper describes an analysis of the diversity and chemical toxicity assessment of three chemical libraries of compounds from African flora (the p-ANAPL, AfroMalariaDb, and Afro-HIV), respectively containing compounds exhibiting activities against...

FABRICATION AND EVALUATION OF SMART NANOCRYSTALS OF ARTEMISININ FOR ANTIMALARIAL AND ANTIBACTERIAL EFFICACY.

African journal of traditional, complementary, and alternative medicines : AJTCAM
BACKGROUND: Nanocrystals have the potential to substantially increase dissolution rate, solubility with subsequent enhanced bioavailability via the oral route of a range of poor water soluble drugs. Regardless of other issues, scale up of the batch s...

Malaria Parasitemia and Parasite Density in Antiretroviral-Treated HIV-Infected Adults Following Discontinuation of Cotrimoxazole Prophylaxis.

The Journal of infectious diseases
BACKGROUND:  Cotrimoxazole (CTX) discontinuation increases malaria incidence in human immunodeficiency virus (HIV)-infected individuals. Rates, quantity, and timing of parasitemia rebound following CTX remain undefined.