Redefining Precision Medicine in Hepatocellular Carcinoma through Omics, Translational, and AI-based Innovations DOI Creative Commons
Rashi Jain, Sathish Kumar Mungamuri, Prabha Garg

и другие.

Опубликована: Май 1, 2025

Язык: Английский

Generative AI Techniques and Models DOI
Rajan T. Gupta, Sanju Tiwari, Poonam Chaudhary

и другие.

Lecture notes on data engineering and communications technologies, Год журнала: 2025, Номер unknown, С. 45 - 64

Опубликована: Янв. 1, 2025

Процитировано

2
A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases DOI Creative Commons

Caterina Vicidomini,

Francesco Fontanella, Tiziana D’Alessandro

и другие.

Biomolecules, Год журнала: 2024, Номер 14(10), С. 1330 - 1330

Опубликована: Окт. 19, 2024

Currently, the age structure of world population is changing due to declining birth rates and increasing life expectancy. As a result, physicians worldwide have treat an number age-related diseases, which neurological disorders represent significant part. In this context, there urgent need discover new therapeutic approaches counteract effects neurodegeneration on human health, computational science can be pivotal importance for more effective neurodrug discovery. The knowledge molecular receptors other biomolecules involved in pathogenesis facilitates design molecules as potential drugs used fight against diseases high social relevance such dementia, Alzheimer's disease (AD) Parkinson's (PD), cite only few. However, absence comprehensive guidelines regarding strengths weaknesses alternative creates fragmented disconnected field, resulting missed opportunities enhance performance achieve successful applications. This review aims summarize some most innovative strategies based methods development. particular, recent applications state-of-the-art docking artificial intelligence ligand- target-based novel drug were reviewed, highlighting crucial role silico context discovery neurodegenerative diseases.

Язык: Английский

Процитировано

8
Exploring prospects, hurdles, and road ahead for generative artificial intelligence in orthopedic education and training DOI Creative Commons
Nikhil Gupta, Kavin Khatri,

Yogender Malik

и другие.

BMC Medical Education, Год журнала: 2024, Номер 24(1)

Опубликована: Дек. 28, 2024

Язык: Английский

Процитировано

5
Modern machine learning methods for protein property prediction DOI

Arjun Dosajh,

P. K. Agrawal,

Prathit Chatterjee

и другие.

Current Opinion in Structural Biology, Год журнала: 2025, Номер 90, С. 102990 - 102990

Опубликована: Янв. 28, 2025

Язык: Английский

Процитировано

0
Animal-skin-pattern-inspired multifunctional composites by generative AI DOI Creative Commons

Milad Masrouri,

Akshay C. Jadhav, Zhao Qin

и другие.

Cell Reports Physical Science, Год журнала: 2025, Номер unknown, С. 102428 - 102428

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Hemicyanine-based fluorescent probes: Advancements in biomedical sensing and activity-based detection DOI
Saleh Muhammad,

Haroon Ahmad,

Yuqian Yan

и другие.

Coordination Chemistry Reviews, Год журнала: 2025, Номер 534, С. 216602 - 216602

Опубликована: Март 14, 2025

Язык: Английский

Процитировано

0
Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery DOI Creative Commons
Margarita Zavadskaya,

A. S. Orlova,

Andrei Dmitrenko

и другие.

Journal of Cheminformatics, Год журнала: 2025, Номер 17(1)

Опубликована: Апрель 15, 2025

Spleen tyrosine kinase (Syk) is a crucial mediator of inflammatory processes and promising therapeutic target for the management autoimmune disorders, such as immune thrombocytopenia. While several Syk inhibitors are known to date, their efficacy safety profiles remain suboptimal, necessitating exploration novel compounds. The study introduces deep reinforcement learning strategy drug discovery, specifically designed identify new inhibitors. approach integrates quantitative structure-activity relationship (QSAR) predictions with generative modelling, employing stacking-ensemble model that achieves correlation coefficient 0.78. From over 78,000 molecules generated by this methodology, we identified 139 candidates high predicted potency, binding affinity optimal drug-likeness properties, demonstrating structural novelty while maintaining essential inhibitor characteristics. Our establishes versatile framework accelerated which particularly valuable development rare disease therapeutics.Scientific contributionThe presents first application QSAR-guided yielding structurally potency. presented methodology can be adapted other targets, potentially accelerating process.

Язык: Английский

Процитировано

0
Redefining Precision Medicine in Hepatocellular Carcinoma through Omics, Translational, and AI-based Innovations DOI Creative Commons
Rashi Jain, Sathish Kumar Mungamuri, Prabha Garg

и другие.

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0