Ligand-based cheminformatics and free energy-inspired molecular simulations for prioritizing and optimizing G-protein coupled receptor kinase-6 (GRK6) inhibitors in multiple myeloma treatment

Computational Biology and Chemistry, Год журнала: 2025, Номер 115, С. 108347 - 108347

Опубликована: Янв. 13, 2025

Язык: Английский

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Exploring QSTR and q-RASTR modeling of agrochemical toxicity on cabbage for environmental safety and human health

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 11, 2025

Язык: Английский

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Artificial intelligence to predict inhibitors of drug-metabolizing enzymes and transporters for safer drug design

Expert Opinion on Drug Discovery, Год журнала: 2025, Номер unknown, С. 1 - 21

Опубликована: Апрель 17, 2025

Drug-metabolizing enzymes (DMEs) and transporters (DTs) play integral roles in drug metabolism drug-drug interactions (DDIs) which directly impact efficacy safety. It is well-established that inhibition of DMEs DTs often leads to adverse reactions (ADRs) therapeutic failure. As such, early prediction such inhibitors vital development. In this context, the limitations traditional vitro assays QSAR models methods have been addressed by harnessing artificial intelligence (AI) techniques. This narrative review presents insights gained from application AI for predicting DME DT over past decade. Several case studies demonstrate successful applications enzyme-transporter interaction prediction, authors discuss workflows integrating these predictions into design regulatory frameworks. The has demonstrated significant potential toward enhancing safety effectiveness. However, critical challenges involve data quality, biases, model transparency. availability diverse, high-quality datasets alongside integration pharmacokinetic genomic are essential. Lastly, collaboration among computational scientists, pharmacologists, bodies pyramidal tailoring tools personalized medicine safer

Язык: Английский

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Exploring fingerprints for antidiabetic therapeutics related to peroxisome proliferator-activated receptor gamma (PPARγ) modulators: A chemometric modeling approach

Computational Biology and Chemistry, Год журнала: 2024, Номер 112, С. 108142 - 108142

Опубликована: Июль 2, 2024

Язык: Английский

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