Recent Applications of the MultiTarget Directed Ligands Approach for the Treatment of Alzheimer's Disease
Bibliographic record
Abstract
In the last years, Medicinal Chemistry is tracking down for novel tools and alternatives capable to bring to light better agility, safety and a more efficient address in the design and prospection of drug candidates.In this context, strategies for drug discovery focusing the development of ligands that act on specific target have been discussed, considering that they have limited application on multifactorial diseases, where a set of biochemical events and protein targets are involved.During the last decade, a novel polypharmacology based approach has emerged for planning ligands, aiming the discovery of chemical entities capable to act simultaneously on multiple molecular targets.Since 2005, the literature has reported many studies that use this innovative strategy for drug design against Alzhei e s disease (AD).AD is a neurodegenerative illness, characterized by a set of interconnected events involving intra and extracellular protein fragment deposits, the onset of a complex neuroinflammatory process, mitochondrial dysfunction, apoptosis and neuronal death.As a consequence of the disease progress, the patient is affected by memory and cholinergic deficits, motor and functional impairment, and death.In this brief review, our goal was to report the most recent advances (2013-2014) on medicinal chemistry for the design and discovery of novel multi-target drug candidate prototypes potentially useful for AD treatment.This work is complementary to another review recently published by our group covering the early 2005-2012 period.In most of the works discussed herein, the commercial drugs rivastigmine, tacrina, donepezil and galanthamine have been used as models of acetilcolinesterase inhibitors for the design of novel molecular hybrids with multiple action profile.In other cases, natural products such as curcumine, resveratrol, berberine and quercetin have been elected due to their neuroprotective, antiapoptotic, anti-inflammatory and antioxidant characteristics for planning new chemical entities with innovative properties and therapeutic potential for a more effective AD treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".