Development of molecular mechanics methods to cover conjugated drug-like molecules for structure based drug design
Bibliographic record
Abstract
Considerable resources and time are required to bring a new drug to the market, in a multidisciplinary process involving structural biologists, synthetic chemists, pharmacologists, among many other experts.It has long been recognized that computation could alleviate costs and human-involvement; and computational tools are now applied to virtually all stages of the drug discovery process.From rigorous statistical analyses of large sets of data or employment of newly emerging artificial intelligence techniques to predict absorption, distribution, metabolism, excretion and toxicity (ADMET) properties among others, to more physically grounded methods I would like to thank Nicolas Moitessier for allowing to enter the fascinating field of computational chemistry without any prior knowledge of programming.Thank you for your guidance over the entire course of my research, for providing your scientific insights when I most needed them, and for proofreading this thesis.I would like to particularly thank Stephen J. Barigye, who helped me tremendously as I first learned to program and initiated my research.Your constructive comments and criticisms have kept me motivated and surely made me a better researcher.Our long and captivating scientific discussions will remain some of my best memories from this degree.I would also like to thank Wanlei Wei with whom it has been a pleasure to collaborate over these past few months, as well as everyone else who has been part of our group during these two years.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".