New virtual screening tools for molecular discovery
Bibliographic record
Abstract
In the field of molecular discovery, virtually screening large libraries of compounds proved to be often more cost-efficient than the traditional experimental approaches. In fact, it has now become common practice thanks to the virtual screening tools available to chemists in the pharmaceutical industry, specifically docking. Most docking programs do not account for the dynamics associated with protein-ligand binding whether it is protein flexibility or the inclusion of displaceable water molecules. FITTED1.0 was developed to include these specific two features and has been validated on a testing set of 33 protein-ligand complexes. Further developments were needed to increase the speed of FITTED to enable its application as virtual screening tool. This enhanced version, FITTED1.5, has been applied to the screening of the Maybridge library onto the HCV polymerase and revealed FITTED’S ability to identify active substances. With this and other successful applications of FITTED, a comparative study was performed against other docking programs, with a specific interest in the effect of the ligand and protein input conformation and the inclusion of bridging water molecules on the accuracy of docking programs. All three had major effects on accuracy and led to suggestions on how to better conduct comparative studies. In parallel, we applied our expertise in the virtual screening area to the field of asymmetric catalyst development and led to the creation of ACE1.0. When creating a tool for predicting steroselectivities, one has to describe the transition state with great accuracy although within a reasonable amount of time. To tackle this problem, ACE creates the transition states from linear combinations of reactant and product interactions. A genetic algorithm is then exploited as a conformational search engine to optimize the TS structure. ACE has been applied to the Diels-Alder cycloaddition and the proline-catalysed aldol reactions and has showed good correlatio
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".