Pruned Receptor Surface Models and Pharmacophores for Three-Dimensional Database Searching
Bibliographic record
Abstract
A pharmacophore represents the 3D arrangement of chemical features that are shared by molecules exhibiting activity at a protein receptor. Pharmacophores are routinely used in 3D database searching for identifying potential lead compounds. The lack of shape constraints causes the query to identify compounds that could not fit into the active site. In the absence of structural information, a receptor surface model (RSM) can be used to represent the active site. The RSM consists of a surface that envelops a set of known actives after these have been aligned using their common features. When used for database searching, a RSM is overconstraining as it restricts access to regions that could be occupied by ligands, such as the solvent-protein interface or unexplored pockets. We describe a protocol for developing pruned RSMs using information gleaned from 3D quantitative structure-activity relationship (QSAR) models. We examined the performance of queries that consist of pharmacophores used alone or with pruned or unpruned RSMs by performing searches on six databases containing known actives distributed among inactives. The pruned RSMs yield an average selectivity 1.8 times greater than that for pharmacophore queries, compared to 1.6 times for unpruned RSMs. However, the pruned RSMs retrieve on average 73% of the actives identified using the pharmacophores, compared to 40% for the unpruned RSMs. As such, pruned RSMs represent a useful compromise between the high sensitivity of pharmacophores and the high selectivity of unpruned RSMs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".