Development and application of chemical tools for the design and synthesis of bioactive molecules
Bibliographic record
Abstract
In the field of drug design and development, medicinal chemists use a variety of tools to quickly generate a series of hit compounds controlling a specific biological target and culminating in a lead compound. Process chemists seek efficient methods to synthesize the lead compounds provided by the medicinal chemists and using readily available and inexpensive starting materials, shortcuts and simpler routes. With this in mind, we aimed to design, develop and apply chemical tools to generate hit compounds, but also developing new simpler methods to make pharmaceutically relevant compounds. In this context, this thesis has two goals. In the first part, we focused on the enzyme prolyl oligopeptidase, reviewing its involvement in neurological disorders, such as Alzheimer's disease, and the efforts of several researchers to synthesize potent, selective inhibitors resembling the natural substrates of this enzyme. We proposed another method, using small pseudopeptidic and peptidomimetic inhibitors as chemical tools to better understand the shape, size and electronics of inhibitors and generate a more potent, selective prolyl oligopeptidase inhibitor. From our series of compounds, we discovered a few potent and highly selective, covalent inhibitors, one of them pseudo-peptidic (IC50 = 3-7 nM) and the other peptidomimetic (IC50 = 20-700 nM). In the second part of this thesis, with the goal of being able to exploit sugars in medicinal chemistry, we first reviewed the methods that exist to regioselectively functionalize the various hydroxyls of hexopyranosides. We compiled these methods into a table which chemists could consult when they are seeking to perform a specific reaction on a specific sugar. We then proposed to use a hydrogen bond accepting protecting group which can direct subsequent reactions to specific sites on sugars in an effort to reduce the number of protection and deprotection steps. We applied this protecting-directing group and developed methods to regio
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".