Current Small Molecule–Based Medicinal Chemistry Approaches for Neurodegeneration Therapeutics
Bibliographic record
Abstract
Neurodegenerative diseases (NDDs) like Alzheimer's disease (AD), Parkinson's disease (PD), and Amyotrophic lateral sclerosis (ALS) possess multifactorial aetiologies. In recent years, our understanding of the biochemical and molecular pathways across NDDs has increased, however, new advances in small molecule-based therapeutic strategies targeting NDDs are obscure and scarce. Moreover, NDDs have been studied for more than five decades, however, there is a paucity of drugs that can treat NDDs. Further, the highly lipoidal blood-brain barrier (BBB) limits the uptake of many therapeutic molecules into the brain and is a complicating factor in the development of new agents to treat neurodegeneration. Considering the highly complex nature of NDDs, the association of multiple risk factors, and the challenges to overcome the BBB junction, medicinal chemists have developed small organic molecule-based novel approaches to target NDDs over the last few decades, such as designing lipophilic molecules and applying prodrug strategies. Attempts have been made to utilize a multitarget approach to modulate different biochemical molecular pathways involved in NDDs, in addition to, medicinal chemists making better decisions in identifying optimized drug candidates for the central nervous system (CNS) by using web-based computational tools. To increase the clinical success of these drug candidates, an in vitro assay modeling the BBB has been utilized by medicinal chemists in the pre-clinical phase as a further screening measure of small organic molecules. Herein, we examine some of the intriguing strategies taken by medicinal chemists to design small organic molecules to combat NDDs, with the intention of increasing our awareness of neurodegenerative therapeutics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".