Bibliographic record
Abstract
INTRODUCTION: Structure-based drug design relies on optimizing drug-target interactions and blocking harmful pathophysiological events at the atomic level. Such events of the human body are modulated by water acting either as a medium or an individual partner in molecular interactions. A precise understanding of the modulatory mechanisms of water is essential for a successful drug design. AREAS COVERED: The present review discusses different topographical and networking situations that result in radically different roles of water, a root of various pitfalls of drug design. The review surveys good practices for tackling the problems of determining water structure at atomic resolution. Techniques for quantifying the effects of bulk, networking, and individual water molecules on the stability of drug-target complexes are also discussed. The article is based on a literature search using the PubMed, Web of Science, and Google Scholar databases. EXPERT OPINION: With advances in rapid computational algorithms and a better understanding of the physicochemical machinery of complex formation, theoretical approaches have resulted in elegant and cost-effective tools that fill the knowledge gaps left by the limited experimental methods. Overcoming the technical pitfalls of drug design, water transforms from a frustrating challenge into a handy tool for fine-tuning drug-target interactions.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".