AIMC Topic: Antimalarials
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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...
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...
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...
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...
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...
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...
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...
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...
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 ...
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