AIMC Topic: Antimalarials

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How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms.

Malaria journal Jul 24, 2025
BACKGROUND: Machine learning algorithms have been used to predict malaria risk and severity, identify immunity biomarkers for malaria vaccine candidates, and determine molecular biomarkers of antimalarial drug resistance. Developing these prediction ...
Artemisinins Drug Resistance Machine Learning Algorithms Plasmodium falciparum Humans Sample Size Antimalarials Malaria, Falciparum
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Computational approaches in drug chemistry leveraging python powered QSPR study of antimalaria compounds by using artificial neural networks.

Scientific reports Jun 2, 2025
The application of Machine Learning has become a revolutionary instrument in the domain of pharmaceutical research. Machine learning enables the modelling of Quantitative Structure Property Relationship, a crucial task in forecasting the physiochemic...
Humans Neural Networks, Computer Antimalarials Quantitative Structure-Activity Relationship Machine Learning Algorithms
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Advancing antimalarial drug discovery: ensemble machine learning models for predicting PfPK6 inhibitor activity.

Molecular diversity Apr 22, 2025
Malaria is a significant global health challenge, causing high morbidity and mortality. The rise of drug resistance highlights the urgent need for new antimalarial agents. This study focuses on predictive modeling of 104 Plasmodium falciparum protein...
Neural Networks, Computer Machine Learning Algorithms Antimalarials Protozoan Proteins Protein Kinase Inhibitors Drug Discovery Support Vector Machine Plasmodium falciparum
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Evaluating large language models as a supplementary patient information resource on antimalarial use in systemic lupus erythematosus.

Lupus Feb 27, 2025
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...
Surveys and Questionnaires Female Lupus Erythematosus, Systemic Humans Patient Education as Topic Reproducibility of Results Language Antimalarials Male Large Language Models
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Novel molecular inhibitor design for Plasmodium falciparum Lactate dehydrogenase enzyme using machine learning generated library of diverse compounds.

Molecular diversity Aug 20, 2024
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...
Machine Learning Drug Design Plasmodium falciparum Small Molecule Libraries Antimalarials Enzyme Inhibitors L-Lactate Dehydrogenase
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MalariaFlow: A comprehensive deep learning platform for multistage phenotypic antimalarial drug discovery.

European journal of medicinal chemistry Aug 16, 2024
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...
Parasitic Sensitivity Tests Plasmodium Phenotype Drug Discovery Deep Learning Molecular Structure Malaria Antimalarials Humans
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Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery.

Journal of chemical information and modeling Jul 16, 2024
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...
Antimalarials Quantitative Structure-Activity Relationship Drug Discovery Quantum Theory Machine Learning Algorithms
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Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool.

Acta parasitologica Jan 2, 2024
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...
Antimalarials Malaria, Falciparum Drug Synergism Drug Therapy, Combination Machine Learning Humans Drug Combinations Plasmodium falciparum
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MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites.

Genome biology Oct 16, 2023
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...
Malaria Antimalarials Malaria, Falciparum Protozoan Proteins Drug Resistance Chromatin Parasites Animals Deep Learning Plasmodium falciparum Humans
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IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds.

International journal of molecular sciences Dec 2, 2021
The parasite species of genus causes Malaria, which remains a major global health problem due to parasite resistance to available Antimalarial drugs and increasing treatment costs. Consequently, computational prediction of new Antimalarial compounds...
Databases, Pharmaceutical Reproducibility of Results Drug Discovery Models, Theoretical Antimalarials Markov Chains Protozoan Proteins Machine Learning Algorithms Plasmodium falciparum Genome, Protozoan Drug Evaluation, Preclinical
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