AI-Driven Prediction of Drug Activity Against Toxoplasma gondii: Data Augmentation and Deep Neural Networks for Limited Datasets

Artificial Intelligence Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 100084 - 100084

Published: Feb. 1, 2025

Language: Английский

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Next-gen senotherapeutics: AI/ML-driven strategies for aging and age-related disorders

Advances in pharmacology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Computer-Aided Drug Design in Research on Chinese Materia Medica: Methods, Applications, Advantages, and Challenges

Pharmaceutics, Journal Year: 2025, Volume and Issue: 17(3), P. 315 - 315

Published: March 1, 2025

Chinese materia medica (CMM) refers to the medicinal substances used in traditional medicine. In recent years, CMM has become globally prevalent, and scientific research on increasingly garnered attention. Computer-aided drug design (CADD) been employed Western medicine for many contributing significantly its progress. However, role of CADD not systematically reviewed. This review briefly introduces methods from perspectives computational chemistry (including quantum chemistry, molecular mechanics, mechanics/molecular mechanics) informatics cheminformatics, bioinformatics, data mining). Then, it provides an exhaustive discussion applications these through rich cases. Finally, outlines advantages challenges research. conclusion, despite current challenges, still offers unique over experiments. With development industry computer science, especially driven by artificial intelligence, is poised play pivotal advancing

Language: Английский

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Practical implementation and impact of the 4R principles in ethnopharmacology: Pursuing a more humane approach to research

Frontiers in Pharmacology, Journal Year: 2025, Volume and Issue: 16

Published: March 28, 2025

Ethnopharmacology, a discipline focused on studying the medicinal use of natural materials by humans, plays crucial role in addressing challenges modern drug development. However, traditional 3R principle-Replacement, Reduction, and Refinement-have limitations guiding ethical management animal experimentation, conducting studies, utilizing animal-derived ethnopharmacological research. To address these gaps, field has introduced 4R principles, which expand original framework adding "Responsibility." The Responsibility principle highlights obligation researchers to consider welfare experimental animals during all procedures. It calls for take accountability their actions decisions, ensuring that they actively protect exhibit empathy across species. This reinforces foundation implement principles effectively, this article explores dimensions Refinement, Replacement, detail. For strategies include minimizing developing optimized, efficient designs, creating tissue banks recycle samples, improving success rates modeling. These efforts collectively aim enhance standards while advancing scientific outcomes. In terms goal is minimize distress pain environment, refining operational procedures, strict control experiments under anesthesia, prioritizing non-invasive or minimally invasive techniques data collection. reduce need exploring alternative solutions. includes substituting vitro vivo ones, using 3D organoids replace organs, applying deep learning technologies ways decrease use. focuses enhancing researchers' obligations toward welfare. can be achieved regulations policies governing providing training technical personnel, promoting awareness practices. introduction implementation provide valuable guidance conduct experimentation research, offering new insights methodologies support responsible studies.

Language: Английский

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Machine learning tools for the characterization of bioactive metabolites derived from different parts of Ochrosia elliptica Labill. for the management of Alzheimer's disease

RSC Advances, Journal Year: 2025, Volume and Issue: 15(14), P. 10671 - 10690

Published: Jan. 1, 2025

Ochroisa elliptica revealed 41 compounds using UPLC-MS/MS and assessed their binding affinities to cholinesterase enzymes through molecular docking. A quercetin derivative exhibited the strongest binding. Additionally, dynamic simulations confirmed stable interactions.

Language: Английский

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