ReviewCurrent topics in medicinal chemistry2026
Computer-aided Drug Design for Alzheimer's Disease: Recent Advancements and Future Perspectives.
Review in Current topics in medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Review
- FDA-Approved Passive Immunization Treatments Against Aβ in Alzheimer's Disease: Where Are We Now?International journal of molecular sciences · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder marked by a decline in cognitive function and memory loss, primarily resulting from cholinergic dysfunction, the accumulation of amyloid plaques, the formation of tau tangles, and the progressive degeneration of neurons. While existing treatments offer limited symptomatic relief, they do not effectively halt or reverse the underlying progression of the disease, presenting a major global challenge in Alzheimer's research. Developing therapeutic strategies for AD remains complex, largely due to the inability of current medications to significantly slow neurodegeneration. Traditional drug discovery processes are often lengthy, costly, and inefficient, further complicating the search for effective treatments. To overcome these obstacles, researchers have turned to a combination of computational approaches alongside conventional drug design techniques. These integrated methodologies help accelerate the discovery process by significantly reducing both time and costs. This review delves into the underlying physiological and pathological mechanisms of Alzheimer's disease, while identifying potential drug targets such as acetylcholinesterase, butyrylcholinesterase, β-Secretase (BACE-1), A2A adenosine receptor, Dickkopf-1 protein, glycogen synthase kinase-3β, indoleamine 2,3-dioxygenase, monoamine oxidase-B, NMDA receptor, Wnt inhibitory factor, cyclindependent kinase-5, glutaminyl cyclase, and cathepsin-B. Furthermore, the review examines various computer-aided drug design (CADD) methodologies, including structure-based and ligandbased approaches, virtual screening, pharmacophore modeling, molecular modelling, and simulation techniques. These computational strategies are playing an increasingly important role in Alzheimer's research, particularly in drug discovery. By investigating promising drug candidates and lead molecules that target key proteins involved in Alzheimer's pathogenesis, the review highlights their binding modes with these targets and assesses the chemical properties essential for the development of effective clinical candidates. The aim is to provide researchers with critical insights and tools to design novel compounds with the necessary chemical and physical characteristics required for the successful treatment of Alzheimer's disease.
Indexed as
Identifiers
40698680What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.