Alzheimer’s Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(3), P. 1004 - 1004

Published: Jan. 24, 2025

Alzheimer’s disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology challenging current treatments. Recent advancements have shifted the focus from traditionally dominant amyloid hypothesis toward multifactorial understanding of disease. Emerging evidence suggests that while amyloid-beta (Aβ) accumulation central to AD, it may not be primary driver but rather part broader pathogenic process. Novel hypotheses been proposed, including role tau protein abnormalities, mitochondrial dysfunction, and chronic neuroinflammation. Additionally, gut–brain axis epigenetic modifications gained attention as potential contributors AD progression. The limitations existing therapies underscore need for innovative strategies. This study explores integration machine learning (ML) in drug discovery accelerate identification novel targets candidates. ML offers ability navigate AD’s complexity, enabling rapid analysis extensive datasets optimizing clinical trial design. synergy between these themes presents promising future more effective

Language: Английский

Virtual screening: hope, hype, and the fine line in between

Expert Opinion on Drug Discovery, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 25, 2025

Technological advancements in virtual screening (VS) have rapidly accelerated its application drug discovery, as reflected by the exponential growth VS-related publications. However, a significant gap remains between volume of computational predictions and their experimental validation. This discrepancy has led to rise number unverified 'claimed' hits which impedes discovery efforts. perspective examines current VS landscape, highlighting essential practices identifying critical challenges, limitations, common pitfalls. Using case studies practices, this aims highlight strategies that can effectively mitigate or overcome these challenges. Furthermore, explores approaches for addressing pharmacodynamic pharmacokinetic issues optimizing hits. become tried-and-true technique due rapid advances methods machine learning (ML) over past two decades. Although each workflow varies depending on chosen approach methodology, integrated combine biological silico data consistently yielded higher success rates. Moreover, widespread adoption ML enhanced integration into pipeline.

Language: Английский

Deep learning in nuclear medicine: from imaging to therapy

Annals of Nuclear Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Language: Английский